Broken Social Contract

March 2024: Analysis

Background

This is a preregistered nationally representative study.

Description

In this project, we examine the role of a broken social contract in people’s trust in institutions and anti-establishment sentiment. Specifically, we identify, through various methods, gaps between what people believe they are promised by the government on paper and what they are being provided by the government in practice. We then examine the explanatory role this gap plays in various socio-political behavioral and attitudinal outcomes.

Hypotheses

1. Similarity between perceived guiding values of the US on paper and perceived guiding values of the US in practice is positively associated with likelihood to vote in the 2024 Presidential Election.
   
2. Similarity between perceived guiding values of the US on paper and perceived guiding values of the US in practice is negatively associated with support for radical change.
   
3. Similarity between perceived guiding values of the US on paper and perceived guiding values of the US in practice is negatively associated with anti-establishment sentiment.
   
4. Similarity between perceived guiding values of the US on paper and perceived guiding values of the US in practice is positively associated with trust in democratic institutions (executive, legislative, and judicial branch).
   
5. Similarity between perceived guiding values of the US on paper and perceived guiding values of the US in practice is positively associated with trust in mainstream societal institutions (media, education, police, military, finance, medicine).

Study design

Two major parts:
1. Social contract: Participants will list the top five guiding values for the US on paper and the top five guiding values for the US in practice. For each of these perspectives, they will assign weights to each value based on its perceived importance to the US on paper and in practice, respectively.
2. Attitudes and individual differences: Participants will complete measures of anti-establishment sentiment, trust in institutions, trust in science, SDO, TIPI, support for radical change, and political identification/behavior.

Analysis plan

The following linear models will be conducted for each of these outcome variables: (1) Likelihood to vote in the 2024 Presidential election; (2) support for radical change; (3) anti-establishment sentiment; (4) trust in democratic institutions; (5) trust in mainstream societal institutions.

1. Correlation matrix: Similarity score, likelihood to vote in the 2024 Presidential election, support for radical change, anti-establishment sentiment, trust in democratic institutions, trust in mainstream societal institutions, trust in science, conservatism, SDO, TIPI extraversion, TIPI agreeableness, TIPI Conscientiousness, TIPI neuroticism, TIPI openness.
2. Linear Model 1: Similarity score as predictor; conservatism as control.
3. Linear Model 2: Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.
4. Linear Model 3: Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.
5. Linear Model 4: Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

Attention Check

But first, let’s exclude participants who failed attention checks: one simple attention check imbedded in the anti-establishment scale and another with incoherent open-text.

df_bsc %>% 
  group_by(check_1,check_2) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

check_1	check_2	N	Perc
0	0	1188	98.59
0	1	14	1.16
1	1	3	0.25

Great. That leaves us with 1188 eligible participants.

Demographics

Race and ethnicity

df_bsc_elg %>% 
  group_by(race,hispanic) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

## `summarise()` has grouped output by 'race'. You can override using the
## `.groups` argument.

race	hispanic	N	Perc
American Indian or Alaska Native	0	1	0.08
Asian	0	53	4.46
Asian	1	1	0.08
Black or African American	0	159	13.38
Black or African American	1	2	0.17
Middle Eastern or North African	0	1	0.08
Middle Eastern or North African	1	1	0.08
Native Hawaiian or Other Pacific Islander	0	1	0.08
Other (please specify)	0	8	0.67
Other (please specify)	1	1	0.08
White	0	760	63.97
White	1	73	6.14
multiracial	0	32	2.69
multiracial	1	4	0.34
NA	1	91	7.66

Gender

df_bsc_elg %>% 
  mutate(gender = ifelse(is.na(gender) | gender == "","other",gender)) %>% 
  group_by(gender) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

gender	N	Perc
man	595	50.08
other	1	0.08
woman	592	49.83

Age

df_bsc_elg %>% 
  summarise(age_mean = round(mean(age,na.rm = T),2),
            age_sd = round(sd(age,na.rm = T),2)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

age_mean	age_sd
45.35	15.85

Education

df_bsc_elg %>% 
  group_by(edu) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

edu	N	Perc
noHS	4	0.34
GED	310	26.09
2yearColl	151	12.71
4yearColl	517	43.52
MA	146	12.29
PHD	57	4.80
NA	3	0.25

Income

df_bsc_elg %>% 
  ggplot(aes(x = income)) +
  geom_bar() +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_blank(),
        axis.title.y = element_blank()) +
  coord_flip()

Politics

Ideology

Participants were asked about the extent to which they subscribe to the following ideologies on a scale of 1-7 (select NA if unfamiliar): Conservatism, Liberalism, Democratic Socialism, Libertarianism, Progressivism.

means <- df_bsc_elg %>%
  dplyr::select(PID,ideo_con:ideo_prog) %>% 
  pivot_longer(-PID,
               names_to = "ideo",
               values_to = "score") %>% 
  filter(!is.na(score)) %>% 
  group_by(ideo) %>% 
  summarise(score = mean(score)) %>% 
  ungroup()

df_bsc_elg %>%
  dplyr::select(PID,ideo_con:ideo_prog) %>% 
  pivot_longer(-PID,
               names_to = "ideo",
               values_to = "score") %>% 
  filter(!is.na(score)) %>%  
  ggplot() +
  geom_density(aes(x = score), fill = "lightblue") +
  scale_x_continuous(limits = c(1,7),
                     breaks = seq(1,7,1)) +
  geom_vline(data = means,mapping = aes(xintercept = score),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold")) +
  facet_wrap(~ideo,nrow = 2)

Party ID

df_bsc_elg %>% 
  group_by(party_id) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

party_id	N	Perc
Democrat	401	33.75
Independent	401	33.75
Republican	386	32.49

Vote in 2020

df_bsc_elg %>% 
  group_by(vote_2020) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

vote_2020	N	Perc
Joe Biden	551	46.38
Donald Trump	411	34.60
I did not vote	176	14.81
Third-party candidate	50	4.21

Vote in 2024

df_bsc_elg %>% 
  group_by(vote_2024) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

vote_2024	N	Perc
Joe Biden	507	42.68
Donald Trump	452	38.05
Robert F. Kennedy Jr.	102	8.59
Other	83	6.99
Cornel West	23	1.94
Jill Stein	20	1.68
	1	0.08

Measures

Open responses

First, I’ll just show the values mentioned for each perspective.

US on paper

df_bsc_long %>%
  left_join(df_bsc_elg %>% 
              dplyr::select(PID) %>% 
              mutate(elg = 1),by = "PID") %>% 
  filter(!is.na(elg)) %>% 
  dplyr::select(type,value) %>%
  filter(type == "uspaper") %>% 
  group_by(value) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  dplyr::select(N,Perc,value) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                position = "left") %>% 
  scroll_box(width = "100%", height = "400px")

N	Perc	value
535	9.01	freedom
398	6.70	equality
312	5.25	liberty
294	4.95	democracy
239	4.02	justice
211	3.55	freedom_of_speech
153	2.58	freedom_of_religion
143	2.41	independence
97	1.63	opportunity
94	1.58	pursuit_of_happiness
67	1.13	diversity
58	0.98	free_speech
58	0.98	individualism
54	0.91	life
52	0.88	fairness
52	0.88	right_to_bear_arms
48	0.81	unity
40	0.67	capitalism
38	0.64	religious_freedom
37	0.62	right_to_vote
34	0.57	happiness
30	0.51	hard_work
28	0.47	peace
28	0.47	rights
26	0.44	progress
26	0.44	religion
25	0.42	individual_rights
25	0.42	limited_government
24	0.40	honesty
24	0.40	integrity
24	0.40	patriotism
24	0.40	prosperity
24	0.40	strength
23	0.39	freedom_of_press
22	0.37	power
22	0.37	privacy
22	0.37	rule_of_law
20	0.34	justice_for_all
20	0.34	safety
18	0.30	family
18	0.30	respect
18	0.30	self_determination
17	0.29	equal_rights
17	0.29	equality_for_all
17	0.29	freedom_of_expression
17	0.29	human_rights
17	0.29	innovation
16	0.27	money
15	0.25	individuality
15	0.25	loyalty
15	0.25	truth
14	0.24	checks_and_balances
14	0.24	individual_freedom
13	0.22	bear_arms
13	0.22	equal_opportunity
13	0.22	security
12	0.20	acceptance
12	0.20	due_process
12	0.20	freedom_of_choice
12	0.20	self_reliance
11	0.19	gun_rights
11	0.19	protection
11	0.19	wealth
10	0.17	achievement
10	0.17	compassion
10	0.17	equity
10	0.17	freedom_of_assembly
10	0.17	guns
10	0.17	honor
10	0.17	sovereignty
9	0.15	choice
9	0.15	community
9	0.15	right_to_assemble
9	0.15	separation_of_powers
9	0.15	speech
8	0.13	education
8	0.13	fair
8	0.13	freedom_of_speach
8	0.13	freedom_to_bear_arms
8	0.13	morality
8	0.13	right_to_arms
8	0.13	self_government
8	0.13	tolerance
7	0.12	autonomy
7	0.12	democratic_government
7	0.12	economic_freedom
7	0.12	free_press
7	0.12	freedom_of_movement
7	0.12	freedom_of_the_press
7	0.12	good
7	0.12	hope
7	0.12	materialism
7	0.12	representation
7	0.12	safe
7	0.12	work
6	0.10	american_dream
6	0.10	bravery
6	0.10	caring
6	0.10	change
6	0.10	federalism
6	0.10	freedom_from_tyranny
6	0.10	freedom_of_thought
6	0.10	freedom_to_assemble
6	0.10	god
6	0.10	government
6	0.10	gun_ownership
6	0.10	law
6	0.10	liberty_for_all
6	0.10	personal_responsibility
6	0.10	pride
6	0.10	representative_democracy
6	0.10	representative_government
6	0.10	right_to_life
5	0.08	all_men_are_created_equal
5	0.08	created_equal
5	0.08	cultural_diversity
5	0.08	entrepreneurship
5	0.08	fair_trial
5	0.08	free_will
5	0.08	freedom_for_all
5	0.08	freedom_to_choose
5	0.08	inclusion
5	0.08	independance
5	0.08	independent
5	0.08	leadership
5	0.08	openness
5	0.08	personal_rights
5	0.08	private_property
5	0.08	property
5	0.08	republic
5	0.08	self_governing
5	0.08	success
5	0.08	values
4	0.07	advancement
4	0.07	all_created_equal
4	0.07	brotherhood
4	0.07	democratic
4	0.07	fair_taxation
4	0.07	for_the_people
4	0.07	freedom_from_oppression
4	0.07	freedom_from_persecution
4	0.07	freedom_from_religion
4	0.07	freedom_of_association
4	0.07	individual_liberty
4	0.07	informality
4	0.07	land_of_opportunity
4	0.07	land_ownership
4	0.07	love
4	0.07	melting_pot
4	0.07	nationalism
4	0.07	patriotic
4	0.07	property_rights
4	0.07	protection_of_citizens
4	0.07	pursuit_of_hapiness
4	0.07	republicanism
4	0.07	right_bear_arms
4	0.07	right_to_free_speech
4	0.07	right_to_privacy
4	0.07	right_to_protest
4	0.07	separation_of_church_and_state
4	0.07	social_mobility
4	0.07	states_rights
4	0.07	the_pursuit_of_happiness
4	0.07	united
3	0.05	ability_to_vote
3	0.05	authority
3	0.05	by_the_people
3	0.05	choices
3	0.05	christian_values
3	0.05	christianity
3	0.05	control
3	0.05	culture
3	0.05	defense
3	0.05	democratic_republic
3	0.05	effective_government
3	0.05	efficiency
3	0.05	equal_justice
3	0.05	equal_opportunities
3	0.05	equality_before_the_law
3	0.05	equality_of_citizens
3	0.05	equality_of_opportunity
3	0.05	everyone_is_equal
3	0.05	fair_elections
3	0.05	faith
3	0.05	free
3	0.05	free_choice
3	0.05	free_market
3	0.05	freedom_of_worship
3	0.05	freedoms
3	0.05	greed
3	0.05	honest
3	0.05	immigrants
3	0.05	indepence
3	0.05	independency
3	0.05	individual_freedoms
3	0.05	individual_responsibility
3	0.05	law_and_order
3	0.05	laws
3	0.05	military_strength
3	0.05	morals
3	0.05	no_taxation_without_representation
3	0.05	one_nation
3	0.05	opportunity_for_all
3	0.05	order
3	0.05	ownership
3	0.05	personal_choice
3	0.05	personal_liberty
3	0.05	protection_from_tyranny
3	0.05	right_to_a_fair_trial
3	0.05	right_to_bare_arms
3	0.05	right_to_happiness
3	0.05	right_to_work
3	0.05	right_to_worship
3	0.05	science
3	0.05	separation_of_power
3	0.05	service
3	0.05	superiority
3	0.05	taxes
3	0.05	the_american_dream
3	0.05	the_right_to_bear_arms
3	0.05	togetherness
3	0.05	trust
3	0.05	value_of_freedom
3	0.05	voting
3	0.05	voting_rights
3	0.05	war
3	0.05	work_ethic
2	0.03	acceptance_of_all
2	0.03	accepting
2	0.03	accountability
2	0.03	all_are_equal
2	0.03	all_men_created_equal
2	0.03	bare_arms
2	0.03	being_an_individual
2	0.03	belief_in_god
2	0.03	brave
2	0.03	care
2	0.03	checks_and_balance
2	0.03	civility
2	0.03	common_good
2	0.03	competition
2	0.03	constitutional_republic
2	0.03	country
2	0.03	democratic_principles
2	0.03	democratic_representation
2	0.03	dignity
2	0.03	diligence
2	0.03	directness
2	0.03	domestic_tranquility
2	0.03	dreams
2	0.03	due_process_of_law
2	0.03	elected_officials
2	0.03	equal_protection
2	0.03	equal_representation
2	0.03	equality_of_all
2	0.03	equality_under_the_law
2	0.03	ethical
2	0.03	everyone_is_created_equal
2	0.03	experience
2	0.03	exploration
2	0.03	fair_representation
2	0.03	free_elections
2	0.03	free_markets
2	0.03	free_speach
2	0.03	freedom_from_tryanny
2	0.03	freedom_of/from_religion
2	0.03	freedom_of_and_from_religion
2	0.03	freedom_of_belief
2	0.03	freedom_of_beliefs
2	0.03	freedom_to_bare_arms
2	0.03	freedom_to_vote
2	0.03	freedom_to_worship
2	0.03	fun
2	0.03	generosity
2	0.03	god_given_rights
2	0.03	growth
2	0.03	hard_working
2	0.03	hardwork
2	0.03	helpful
2	0.03	humanity
2	0.03	idk
2	0.03	inalienable_rights
2	0.03	individual_right
2	0.03	innocent_until_proven_guilty
2	0.03	kindness
2	0.03	law_abiding
2	0.03	leader
2	0.03	liberty_and_equality
2	0.03	loyal
2	0.03	na
2	0.03	no_slavery
2	0.03	oligarchy
2	0.03	open
2	0.03	opportunities
2	0.03	optimism
2	0.03	personal_freedom
2	0.03	persuit_of_happiness
2	0.03	powerful
2	0.03	property_ownership
2	0.03	racism
2	0.03	reality
2	0.03	recognition
2	0.03	representation_in_government
2	0.03	resilience
2	0.03	responsibility
2	0.03	responsible
2	0.03	right_of_assembly
2	0.03	right_to_a_fair_and_speedy_trial
2	0.03	right_to_assembly
2	0.03	right_to_fair_trial
2	0.03	right_to_live
2	0.03	right_to_own_land
2	0.03	right_to_property
2	0.03	right_to_safety
2	0.03	right_to_trial
2	0.03	rights_for_all
2	0.03	rights_of_citizens
2	0.03	self_defense
2	0.03	self_expression
2	0.03	self_govern
2	0.03	self_governance
2	0.03	self_protection
2	0.03	self_reliant
2	0.03	seperation_of_church_and_state
2	0.03	small_government
2	0.03	stability
2	0.03	strong_economy
2	0.03	strong_military
2	0.03	suffrage
2	0.03	taxation_with_representation
2	0.03	technology
2	0.03	territory
2	0.03	together
2	0.03	tradition
2	0.03	trustworthy
2	0.03	understanding
2	0.03	upward_mobility
2	0.03	value
2	0.03	welcoming
2	0.03	worship
1	0.02	_government_for_the_people
1	0.02	_speedy_and_fair_trial
1	0.02	_value_of_democracy
1	0.02	_value_of_privacy
1	0.02	_voting_rights
1	0.02	a_check_and_balance_system
1	0.02	a_constitutional_democracy
1	0.02	a_democratic_process_for_electing_our_goverment
1	0.02	a_fair_justice_system
1	0.02	a_good_life
1	0.02	a_nation_of_laws
1	0.02	a_right_to_free_speech
1	0.02	ability_to_achieve
1	0.02	ability_to_choose
1	0.02	ability_to_chose_representation
1	0.02	ability_to_earn_a_living
1	0.02	ability_to_facer_accusers
1	0.02	ability_to_live_where_you_want
1	0.02	ability_to_mix_cultures
1	0.02	ability_to_move_about_the_country
1	0.02	ability_to_move_up
1	0.02	ability_to_own
1	0.02	ability_to_own_land
1	0.02	ability_to_participate
1	0.02	ability_to_practice_religion
1	0.02	ability_to_prosper
1	0.02	ability_to_reverse_set_laws
1	0.02	ability_to_succeed
1	0.02	accepting_of_everyone
1	0.02	access
1	0.02	accesss_to_healthcare
1	0.02	accomodating
1	0.02	accomplishment
1	0.02	accountability_is_necessary
1	0.02	achieve_success
1	0.02	achievement_and_success
1	0.02	achievement_oriented
1	0.02	action
1	0.02	activity_and_work
1	0.02	adaptability
1	0.02	advocacy
1	0.02	agency
1	0.02	alienable_rights
1	0.02	all_legal_citizens_are_allowed_to_vote
1	0.02	all_men_are_created_equally
1	0.02	all_men_are_equal
1	0.02	all_men_equal
1	0.02	all_people_are_created_equal
1	0.02	all_sorts_rubbish_like_lesbian
1	0.02	allegiance
1	0.02	almost_biblical_to_a_point_:)
1	0.02	also_liberty
1	0.02	altruism
1	0.02	always_equal_opportunity
1	0.02	ambitious
1	0.02	amendments_for_all
1	0.02	america_values_diversity
1	0.02	america_values_equality
1	0.02	america_values_independence
1	0.02	america_values_liberty
1	0.02	america_values_unity
1	0.02	and_justice
1	0.02	anti_authoritarianism
1	0.02	anti_discrimination
1	0.02	anti_monarchy
1	0.02	anti_socialism
1	0.02	antiracism
1	0.02	appearance
1	0.02	arm_oneself
1	0.02	arms
1	0.02	arms_freedom
1	0.02	as_americans_we_have_rights
1	0.02	aspiration/upward_mobility
1	0.02	assembly
1	0.02	assertiveness
1	0.02	assisting_others
1	0.02	autonomy_for_all
1	0.02	balance_of_power
1	0.02	balance_of_powers
1	0.02	balance_of_powers_(gov’t)
1	0.02	balanced_government
1	0.02	balanced_power_between_the_branches_of_government
1	0.02	basic_human_rights
1	0.02	be_as_successful_as_your_abilities_allow.
1	0.02	bearing_arms
1	0.02	being_able_to_choose_who_you_marry
1	0.02	being_able_to_own_land
1	0.02	being_accepting
1	0.02	being_an_ally
1	0.02	being_brave
1	0.02	belief
1	0.02	belief_in_military
1	0.02	benevolence
1	0.02	bigotry
1	0.02	bill_of_rights
1	0.02	bill_of_rights.
1	0.02	blank
1	0.02	boldness
1	0.02	bullying
1	0.02	business_opportunities
1	0.02	can_critique_government
1	0.02	capable
1	0.02	capableness
1	0.02	capacity_for_progress
1	0.02	capitalism_built_this_country
1	0.02	capitalist
1	0.02	capitalistic_freedom
1	0.02	capitlism
1	0.02	captialism
1	0.02	care_for_others
1	0.02	care_for_vulnerable
1	0.02	caring_spirit
1	0.02	carry_a_gun
1	0.02	causing_war
1	0.02	change_what_was
1	0.02	charity
1	0.02	checks_and_balanaces
1	0.02	checks_and_balances.
1	0.02	choice_in_religion
1	0.02	choice_of_elections
1	0.02	choose_career
1	0.02	choose_religion
1	0.02	chriistianity
1	0.02	christian_faith
1	0.02	church/state_separation
1	0.02	citizens_safety
1	0.02	citizenship
1	0.02	civic_work
1	0.02	civil_authority_over_military
1	0.02	civil_freedom
1	0.02	civil_liberties
1	0.02	civil_liberty
1	0.02	civil_rights
1	0.02	class_caste_hierachy
1	0.02	class_fluidity
1	0.02	classism
1	0.02	collaboration_in_decisions
1	0.02	collective_defense
1	0.02	collective_freedom
1	0.02	collective_responsibility
1	0.02	colonialism
1	0.02	commerce
1	0.02	commitment
1	0.02	communication
1	0.02	communism
1	0.02	compassion_for_others
1	0.02	compassioniate_of_immigrants
1	0.02	competitiveness
1	0.02	connected
1	0.02	connections
1	0.02	consent_of_governed
1	0.02	consent_of_the_governed
1	0.02	conservation
1	0.02	constitution
1	0.02	constitutional
1	0.02	continues_improvement_and_advancement
1	0.02	control_over_citizens
1	0.02	controlled_government
1	0.02	corruption
1	0.02	courage
1	0.02	creativity
1	0.02	creedy
1	0.02	cults_and_extremists_are_taking_over
1	0.02	cultural
1	0.02	cultural_differences
1	0.02	cultural_dominance
1	0.02	cultural_values
1	0.02	culturalization
1	0.02	culture_blended
1	0.02	currency
1	0.02	decentralization
1	0.02	defend_our_nation
1	0.02	defender_of_meek
1	0.02	defense_for_all
1	0.02	defensive
1	0.02	degradation
1	0.02	delegation_of_powers
1	0.02	deluded_spirituality
1	0.02	democcratic_republic_philosophy
1	0.02	democracry
1	0.02	democracy:people’s_voice_heard
1	0.02	democracy_and_enterprise
1	0.02	democracy_e.g._power_is_with_“the_people”.
1	0.02	democracy_in_general_as_the_overriding_principal
1	0.02	democracy_in_goverment
1	0.02	democracy_in_government
1	0.02	democracy_is_also_an_important_value
1	0.02	democrat_elections
1	0.02	democratic_consensus
1	0.02	democratic_power_of_government
1	0.02	democratic_process
1	0.02	democratic_processes
1	0.02	democratic_society
1	0.02	democratic_values
1	0.02	democratically_elected_leadership.
1	0.02	democratically_selected_representation
1	0.02	deocracy
1	0.02	desire_to_achieve
1	0.02	determination
1	0.02	deviancy
1	0.02	devotion_to_family
1	0.02	disability_protections
1	0.02	discipline
1	0.02	discovery
1	0.02	discrimination
1	0.02	diverse
1	0.02	diverse_but_partisan
1	0.02	diverse_population
1	0.02	diversity_amongst_us
1	0.02	diversity_in_religions
1	0.02	diversity_of_citizenry.
1	0.02	diversity_of_ideas.
1	0.02	diverstiy
1	0.02	divided_political_parties
1	0.02	divirsity
1	0.02	do_not_kill
1	0.02	do_not_steal
1	0.02	do_on_to_others_as_you_do_to_yourself.
1	0.02	dominance_over_other_countries
1	0.02	domination
1	0.02	dream
1	0.02	dream_and_hardwork
1	0.02	dream_big
1	0.02	due_process_protecton
1	0.02	due_process_under_the_law
1	0.02	duty
1	0.02	economic_decisions
1	0.02	economic_fredum
1	0.02	economic_frredom
1	0.02	economic_liberty
1	0.02	economic_opportunity
1	0.02	economic_oppurtunity
1	0.02	egalitarianism
1	0.02	elected_representation
1	0.02	electing_officials
1	0.02	election_of_officials
1	0.02	elections_by_people
1	0.02	empathy
1	0.02	emphasis_on_individualism.
1	0.02	enjoyment
1	0.02	ensuring_protection_of_personal_freedoms
1	0.02	entrepreneurial
1	0.02	equaity
1	0.02	equal_and_fair_treatment
1	0.02	equal_chance_at_success
1	0.02	equal_justice_under_the_law
1	0.02	equal_opportunity_for_all
1	0.02	equal_opportunity_for_all.
1	0.02	equal_opportunity_under_the_law
1	0.02	equal_opprotinties
1	0.02	equal_pay
1	0.02	equal_races
1	0.02	equal_rights_and_opportunities
1	0.02	equal_rights_for_all
1	0.02	equal_rights_under_the_law
1	0.02	equal_standing
1	0.02	equal_under_law
1	0.02	equality:_fair_treatment_for_all
1	0.02	equality_(not_equity)
1	0.02	equality_among_all_citizens
1	0.02	equality_among_people
1	0.02	equality_amongst_all
1	0.02	equality_before_the_law_of_individuals
1	0.02	equality_between_all
1	0.02	equality_is_another_important_value_that_the_u.s_stands_for
1	0.02	equality_of_all_human_being
1	0.02	equality_of_all_people
1	0.02	equality_of_humans
1	0.02	equality_of_persons
1	0.02	equality_of_races
1	0.02	equality_under_law
1	0.02	equalness
1	0.02	equility
1	0.02	equitable_rights
1	0.02	equity_and_justice
1	0.02	ethical_conduct_by_all.
1	0.02	every_individual_is_created_equally.
1	0.02	every_vote_counts
1	0.02	everyone_deserves_a_fair_trial
1	0.02	everyone_equal_under_the_law
1	0.02	everyone_is_welcome
1	0.02	everyone_right_to_happiness
1	0.02	excellence
1	0.02	exchange
1	0.02	executive_legislative
1	0.02	expensive
1	0.02	expressing_opinion.
1	0.02	expression
1	0.02	fair_dei_practices
1	0.02	fair_employment
1	0.02	fair_judicial_system
1	0.02	fair_justice_system
1	0.02	fair_trade
1	0.02	fair_trade_laws
1	0.02	fair_trail
1	0.02	fair_treatment
1	0.02	fair_treatment_of_all
1	0.02	fair_trials
1	0.02	fair_wages
1	0.02	fairness_for_all
1	0.02	fairness_of_taxation
1	0.02	fairness_to_workers
1	0.02	fairness_towards_all
1	0.02	fairness_under_law
1	0.02	faith_in_god
1	0.02	faithful
1	0.02	fame
1	0.02	family_life/privacy
1	0.02	family_tradition
1	0.02	family_unit
1	0.02	family_values
1	0.02	faux_multiculturalism
1	0.02	fear_god
1	0.02	federalist_structure
1	0.02	fight_for_freedom
1	0.02	fighting_for_the_weaker
1	0.02	financial
1	0.02	first_amendment
1	0.02	first_amendment_rights
1	0.02	for_all
1	0.02	forming_militias
1	0.02	foudning_fathers
1	0.02	foundation
1	0.02	fourth_amendment
1	0.02	fraternity
1	0.02	fredom_of_the_press
1	0.02	free_and_fair_elections
1	0.02	free_and_fair_expression_without_repercussions
1	0.02	free_assembly
1	0.02	free_association
1	0.02	free_elections,
1	0.02	free_enterprise
1	0.02	free_expression
1	0.02	free_expression_of_ideas
1	0.02	free_from_dictatorship__
1	0.02	free_from_predjudice
1	0.02	free_from_racism
1	0.02	free_from_tirany
1	0.02	free_life
1	0.02	free_market_economy
1	0.02	free_movement
1	0.02	free_of_persecution
1	0.02	free_religion
1	0.02	free_religions
1	0.02	free_speech_for_republicans
1	0.02	free_thinkers
1	0.02	free_thinking
1	0.02	free_to_work
1	0.02	free_to_worship
1	0.02	free_trade
1	0.02	free_travel
1	0.02	freedom,_overall
1	0.02	freedom,_self_reliance
1	0.02	freedom/free_will
1	0.02	freedom/liberty
1	0.02	freedom_assembly
1	0.02	freedom_bear_arms
1	0.02	freedom_for_a_fair_trail___innocent_until_proven_guilty
1	0.02	freedom_for_its_citizens
1	0.02	freedom_for_people
1	0.02	freedom_for_self_governing
1	0.02	freedom_for_speech
1	0.02	freedom_from_a_ruling_elite_class___no_dictators_or_monarchs
1	0.02	freedom_from_discrimination
1	0.02	freedom_from_foreign_interference
1	0.02	freedom_from_government
1	0.02	freedom_from_government_tyrany
1	0.02	freedom_from_intrusion
1	0.02	freedom_from_religious_persecution
1	0.02	freedom_from_slavery
1	0.02	freedom_from_social_class
1	0.02	freedom_in_everything
1	0.02	freedom_in_general
1	0.02	freedom_in_life
1	0.02	freedom_not_selfincrimnate
1	0.02	freedom_of_all
1	0.02	freedom_of_arms
1	0.02	freedom_of_assiciaton
1	0.02	freedom_of_commerce
1	0.02	freedom_of_conscience
1	0.02	freedom_of_education
1	0.02	freedom_of_equality
1	0.02	freedom_of_expression,_religion_and_from_religion
1	0.02	freedom_of_guns
1	0.02	freedom_of_individual_speech
1	0.02	freedom_of_justice
1	0.02	freedom_of_liberty
1	0.02	freedom_of_life
1	0.02	freedom_of_living
1	0.02	freedom_of_oppurtunity
1	0.02	freedom_of_ownership
1	0.02	freedom_of_ownership_of_property_which_is_protected_from_government_seizure
1	0.02	freedom_of_peaceful_assembly
1	0.02	freedom_of_privacy
1	0.02	freedom_of_religionn
1	0.02	freedom_of_religious_practices
1	0.02	freedom_of_religious_preference
1	0.02	freedom_of_rights
1	0.02	freedom_of_seech
1	0.02	freedom_of_self
1	0.02	freedom_of_speech,
1	0.02	freedom_of_speech/expression
1	0.02	freedom_of_speech_(and_press)
1	0.02	freedom_of_speech_and_freedom_of_expression.
1	0.02	freedom_of_trade
1	0.02	freedom_of_work
1	0.02	freedom_og_speech
1	0.02	freedom_overall
1	0.02	freedom_press
1	0.02	freedom_right_to_arms
1	0.02	freedom_search_and_seizure
1	0.02	freedom_to_achieve
1	0.02	freedom_to_be_your_own_person
1	0.02	freedom_to_be_your_true_self
1	0.02	freedom_to_carry_arms
1	0.02	freedom_to_change
1	0.02	freedom_to_choose_a_way_of_life
1	0.02	freedom_to_congregate
1	0.02	freedom_to_defend_ourselves
1	0.02	freedom_to_defend_yourself_with_guns
1	0.02	freedom_to_earn_your_own_wealth
1	0.02	freedom_to_exist
1	0.02	freedom_to_gather
1	0.02	freedom_to_go_against_authorities
1	0.02	freedom_to_incite_riots
1	0.02	freedom_to_live
1	0.02	freedom_to_move
1	0.02	freedom_to_own_property
1	0.02	freedom_to_peaceably_assemble
1	0.02	freedom_to_practice_individual_belief_system_values
1	0.02	freedom_to_practice_religion
1	0.02	freedom_to_practice_religion_as_you_choose
1	0.02	freedom_to_protect
1	0.02	freedom_to_protect_myself
1	0.02	freedom_to_protect_ones_property
1	0.02	freedom_to_purchase
1	0.02	freedom_to_pursue_your_dreams
1	0.02	freedom_to_put_bible_back_in_school
1	0.02	freedom_to_religion
1	0.02	freedom_to_speak
1	0.02	freedom_to_succeed_or_fail
1	0.02	freedom_to_teach_children_respect
1	0.02	freedom_to_travel
1	0.02	freedom_to_voice_your_opinion
1	0.02	freedom_to_wear_weapons
1	0.02	freedom_to_worship_god
1	0.02	freedom_to_worship_how_and_where_you_want
1	0.02	freedom_values
1	0.02	freedoms_and_rights
1	0.02	freedon_of_speech
1	0.02	freedoom
1	0.02	freedpm_of_religion
1	0.02	friendliness
1	0.02	friendly
1	0.02	frreedom
1	0.02	fundamental_human_right
1	0.02	future
1	0.02	giving
1	0.02	gluttony
1	0.02	god,_country_and_family
1	0.02	god_loving_nation
1	0.02	godliness
1	0.02	good_education
1	0.02	good_governance
1	0.02	good_healthcare
1	0.02	good_intentions
1	0.02	goodwill
1	0.02	govenrment_works_for_the_people
1	0.02	governed_by_democracy
1	0.02	governing_bodies
1	0.02	government_by_the_people
1	0.02	government_for_people
1	0.02	government_for_the_people
1	0.02	government_of_the_people
1	0.02	government_of_the_people,_for_the_people,by_the_people(no_kings)
1	0.02	government_oversight
1	0.02	government_restraints
1	0.02	great
1	0.02	greatness
1	0.02	guards_against_factions
1	0.02	gun_right
1	0.02	gun_values
1	0.02	happy
1	0.02	hard,_honest_work_will_reward_you_with_prosperity.
1	0.02	hard_work_pays
1	0.02	hard_work_rewarded
1	0.02	hardworking
1	0.02	hate
1	0.02	having_a_democracy
1	0.02	having_diversity
1	0.02	having_equal_rights
1	0.02	having_equality
1	0.02	having_freedom
1	0.02	having_individualism
1	0.02	having_justice_for_all
1	0.02	having_rights
1	0.02	having_the_peoples_voice
1	0.02	having_unity
1	0.02	healthcare
1	0.02	helping
1	0.02	helping_others
1	0.02	helping_the_poor
1	0.02	helping_your_fellow_man
1	0.02	hierachy_by_skin
1	0.02	high_achievement
1	0.02	high_market_value
1	0.02	high_moral_standards
1	0.02	home
1	0.02	home_ownership
1	0.02	homosexual_agenda
1	0.02	honesty_and_respect
1	0.02	honor_among_countrymen
1	0.02	human_advancement
1	0.02	human_dignity
1	0.02	human_right
1	0.02	humanitarianism
1	0.02	humility
1	0.02	hurting_women
1	0.02	i_have_no_idea
1	0.02	idea_everyone_can_succeed_and_move_up_in_society
1	0.02	idealism
1	0.02	idividualism
1	0.02	illusion_of_freedom
1	0.02	immigrant_nation
1	0.02	immigration
1	0.02	imperialism
1	0.02	improvement
1	0.02	inclusion_of_all
1	0.02	inclusion_of_all_diversity
1	0.02	inclusive
1	0.02	inclusiveness
1	0.02	inclusiveness_values
1	0.02	incorporating_diversity
1	0.02	indepdence
1	0.02	indepedence
1	0.02	independace
1	0.02	independence_of_spirit_and_body
1	0.02	independencie
1	0.02	independent_of_government
1	0.02	independent_thinking
1	0.02	indepenedence
1	0.02	indiscrimination.
1	0.02	individual_autonomy
1	0.02	individual_before_government
1	0.02	individual_freedom.
1	0.02	individual_liberties
1	0.02	individual_right_should_not_be_infringed
1	0.02	individual_rights_before_government_rights
1	0.02	individual_values
1	0.02	individualist
1	0.02	individuality_and_privacy
1	0.02	individuals_over_group
1	0.02	indivisible
1	0.02	indivuality
1	0.02	industriousness
1	0.02	industry
1	0.02	inegrity
1	0.02	inflation
1	0.02	influence
1	0.02	influence_leadership
1	0.02	ingenuity
1	0.02	injustice
1	0.02	innovative
1	0.02	inside_jobs
1	0.02	insure_domestic_tranquility
1	0.02	integrity_as_a_nation
1	0.02	integrity_in_actions
1	0.02	intellectual_curiosity
1	0.02	intelligence
1	0.02	interest_in_the_overall_welfare_of_the_nation
1	0.02	it_stand_for_democracy
1	0.02	it_stand_for_equality
1	0.02	joy
1	0.02	judgement
1	0.02	judicial
1	0.02	jury_of_your_peers_for_offensives
1	0.02	jury_trial
1	0.02	jusice
1	0.02	jusitice
1	0.02	just_society
1	0.02	justice:_rule_of_law_upheld__
1	0.02	justice_and_fairness.
1	0.02	justice_and_truth
1	0.02	justice_for_all_groups
1	0.02	justice_for_for_all_irrespective_of_status
1	0.02	justice_in_all_actions
1	0.02	justice_is_another_important_stands
1	0.02	justice_the_same_for_everyone
1	0.02	justices
1	0.02	justics
1	0.02	justness
1	0.02	keep_bear_arms
1	0.02	keep_us_poor
1	0.02	keeping_family_bonds
1	0.02	keeping_the_rich_as_rich_as_possible
1	0.02	kind
1	0.02	knowledge_that_your_offspring_will_live_in_a_better_world_than_you
1	0.02	labor
1	0.02	land_of_opportunities
1	0.02	land_of_the_free
1	0.02	law_&_order
1	0.02	law_and_justice
1	0.02	lawful
1	0.02	lawful_rule
1	0.02	learning
1	0.02	leave_most_things_to_the_states
1	0.02	legal_equality
1	0.02	legal_rights
1	0.02	liberalism
1	0.02	liberation
1	0.02	liberation_from_oppression
1	0.02	liberties
1	0.02	liberty:_individual_freedom_and_rights
1	0.02	liberty_and_freedom
1	0.02	liberty_and_justice
1	0.02	liberty_is_important
1	0.02	liberty_is_very_important_value_that_u.s_stands_for
1	0.02	liberty_to_all
1	0.02	liberty_to_be_safe
1	0.02	liberty_to_choose
1	0.02	libery
1	0.02	lies,_and_slander.
1	0.02	life,_liberty
1	0.02	life,liberty&_pursuit_of_happiness
1	0.02	life_liberty_happiness
1	0.02	limited_federal_power
1	0.02	limited_gov’t
1	0.02	limited_goverment
1	0.02	limited_government/constitutionalism
1	0.02	limited_government_power.
1	0.02	limited_governmental_oversight
1	0.02	limited_power
1	0.02	limited_powers_of_government
1	0.02	limited_powers_of_govt
1	0.02	live_free
1	0.02	live_safe
1	0.02	live_without_fear
1	0.02	living_as_a_democratic_society
1	0.02	logic
1	0.02	looking_to_progress
1	0.02	lots_of_housing
1	0.02	lots_of_jobs
1	0.02	lots_of_opportunities
1	0.02	love_and_loyalty_to_country_
1	0.02	love_for_country
1	0.02	love_for_god
1	0.02	love_to_another
1	0.02	low_taxation
1	0.02	low_taxes
1	0.02	loyality
1	0.02	loyalty_to_country
1	0.02	loyalty_with_citizens
1	0.02	make_own_decisions
1	0.02	many_opportunities
1	0.02	market_capitalism
1	0.02	marriage_equality
1	0.02	materialistic
1	0.02	melting_pot_of_peoples
1	0.02	meritocracy
1	0.02	military_preparedness
1	0.02	militia
1	0.02	mixing_pot/diversity
1	0.02	model_behavior
1	0.02	modern_lifestyle
1	0.02	modern_values
1	0.02	money_for_elites
1	0.02	moral_duty
1	0.02	moral_principles
1	0.02	moral_standards
1	0.02	morality_for_people
1	0.02	movement,
1	0.02	multiculture
1	0.02	narcissitic
1	0.02	nation_under_god
1	0.02	national_parks
1	0.02	national_pride
1	0.02	national_unity
1	0.02	nationalism_to_country
1	0.02	natural_law
1	0.02	natural_rights
1	0.02	nature
1	0.02	new_chance
1	0.02	new_ideas
1	0.02	ninth_amendment
1	0.02	no_cruel_and_unusual_punishment
1	0.02	no_cruel_punishment
1	0.02	no_discrimination
1	0.02	no_double_jeopardy
1	0.02	no_double_standards
1	0.02	no_one_is_higher_than_the_rule_of_law
1	0.02	no_overarching_religion
1	0.02	no_overbearing_policies
1	0.02	no_search_and_seizure
1	0.02	no_search_seizure
1	0.02	no_taxation_withut_representation
1	0.02	no_unreasonable_search_and_seizure
1	0.02	no_unwarranted_persecution
1	0.02	non_discrimination
1	0.02	not_having_every_aspect_of_life_controlled_by_psychotic_politicians_and_bureaucrats
1	0.02	not_putting_the_world_in_order
1	0.02	nuclear_family
1	0.02	obedience
1	0.02	oil
1	0.02	one_man_one_vote
1	0.02	one_nation_under_god
1	0.02	one_nation_undivided
1	0.02	open_and_encouraging
1	0.02	open_and_fair_elections
1	0.02	open_borders
1	0.02	open_market
1	0.02	open_minded
1	0.02	open_mindedness
1	0.02	open_society
1	0.02	opinions
1	0.02	opportunistic
1	0.02	opportunites
1	0.02	opportunities_are_many
1	0.02	opportunities_for_all
1	0.02	opportunities_for_jobs
1	0.02	opportunities_for_success
1	0.02	opportunity:_equal_chances_for_success__
1	0.02	opportunity_for_all_that_work_hard
1	0.02	opportunity_for_growth
1	0.02	opportunity_in_life
1	0.02	opportunity_to_rise_as_far_in_society_as_one_desires
1	0.02	opportunity_to_succeed
1	0.02	oppresive_goverment
1	0.02	oppurtunity
1	0.02	opress_women
1	0.02	other_rights
1	0.02	our_freedom
1	0.02	overthrowing_corrupt_governments
1	0.02	overwork_workers
1	0.02	own_property
1	0.02	owning_a_gun
1	0.02	owning_private_property
1	0.02	patience
1	0.02	patriarchy
1	0.02	patriot
1	0.02	peace_keeper
1	0.02	peaceful_assembly
1	0.02	peacetime
1	0.02	people_are_the_ones_who_determine_the_government.
1	0.02	people_focused_government
1	0.02	people_over_government
1	0.02	peoples_can_speak_up
1	0.02	persistence
1	0.02	personal_independence
1	0.02	personal_privacy
1	0.02	plentiful_opportunities
1	0.02	pluralism
1	0.02	political_achievements
1	0.02	political_freedom
1	0.02	political_manipulation
1	0.02	poor_education
1	0.02	poor_healthcare
1	0.02	popular_sovereinty
1	0.02	population
1	0.02	population_control
1	0.02	popultion
1	0.02	power_for_the_rich
1	0.02	power_of_people
1	0.02	power_of_the_people
1	0.02	power_sharing
1	0.02	practice_my_religion
1	0.02	practice_of_faith
1	0.02	presumed_innocence
1	0.02	printing_excess_currencies
1	0.02	private_ownership
1	0.02	privilage
1	0.02	productivity
1	0.02	profit_over_empathy
1	0.02	progess
1	0.02	progression
1	0.02	prohibit_excessive_fines_or_bail
1	0.02	promote_general_welfare
1	0.02	promote_the_general_welfare
1	0.02	propaganda
1	0.02	property_rights,_ownership
1	0.02	property_rights_protected
1	0.02	prosperity_for_all
1	0.02	prosperity_opportunities
1	0.02	prosperous_working_hard
1	0.02	prosuit_of_happiness
1	0.02	protect_citizens
1	0.02	protect_our_people
1	0.02	protect_the_united_states
1	0.02	protect_your_family
1	0.02	protect_your_ideas
1	0.02	protected
1	0.02	protecting_own_interests
1	0.02	protecting_the_innocent
1	0.02	protection_(“defenseless”/“poor”)
1	0.02	protection_domestic_and_international.
1	0.02	protection_from_govenment
1	0.02	protection_from_invaders
1	0.02	protection_from_unreasonable_search_and_seizure
1	0.02	protection_of_country
1	0.02	protection_of_the_citizens
1	0.02	protection_of_the_weak
1	0.02	protection_under_law
1	0.02	protections
1	0.02	protects_the_liberties_of_citizens
1	0.02	protestant_values
1	0.02	protestant_work_ethic
1	0.02	proud
1	0.02	provide_common_defense
1	0.02	provide_for_common_defense
1	0.02	public_education
1	0.02	purity
1	0.02	pursue_own_interests
1	0.02	pursuit_of_goals
1	0.02	pursuit_of_liberty
1	0.02	pusuit_of_happiness
1	0.02	quality
1	0.02	quality_education
1	0.02	quality_of_life
1	0.02	racial_disparities
1	0.02	raising_families
1	0.02	rational_thinking
1	0.02	reform
1	0.02	reign_in_democracy
1	0.02	relgious_freedom
1	0.02	religiosity
1	0.02	religious._freedom
1	0.02	religious_choice
1	0.02	religious_choices
1	0.02	religious_freedom_for_all
1	0.02	religious_freedoms
1	0.02	religious_life
1	0.02	religious_rights
1	0.02	representation_of_citizens
1	0.02	republicism
1	0.02	respect_for_all
1	0.02	respect_for_authority
1	0.02	respect_of_freedom
1	0.02	respect_of_law
1	0.02	respect_of_laws
1	0.02	respect_others
1	0.02	respectful
1	0.02	respectful_of_faith
1	0.02	reward_for_work
1	0.02	rewarded_for_efforts
1	0.02	rich
1	0.02	right
1	0.02	right_by_trial
1	0.02	right_of_opinon
1	0.02	right_of_self
1	0.02	right_ot_bear_arms
1	0.02	right_to_a_fair_trial_by_jury
1	0.02	right_to_a_speedy_trial
1	0.02	right_to_address_the_government_for_a_redress_of_grieveances
1	0.02	right_to_arm
1	0.02	right_to_assembly/protest
1	0.02	right_to_be_secure
1	0.02	right_to_be_tried_by_my_peers
1	0.02	right_to_bear_arm
1	0.02	right_to_carry
1	0.02	right_to_choose
1	0.02	right_to_defend/bear_arms
1	0.02	right_to_defend_yourself
1	0.02	right_to_disagree
1	0.02	right_to_education
1	0.02	right_to_fair_self_defense
1	0.02	right_to_free_elections
1	0.02	right_to_free_enterprise
1	0.02	right_to_freedom_religion
1	0.02	right_to_guns
1	0.02	right_to_have_privacy
1	0.02	right_to_life,_liberty_and_pursuit_of_happiness
1	0.02	right_to_make_decisions
1	0.02	right_to_make_your_own_choices
1	0.02	right_to_meet_openly
1	0.02	right_to_our_own_opinions
1	0.02	right_to_own
1	0.02	right_to_participate_in_political_elections
1	0.02	right_to_petition
1	0.02	right_to_petition_government_for_redress_of_grievances
1	0.02	right_to_petition_the_government
1	0.02	right_to_practice_religion
1	0.02	right_to_press
1	0.02	right_to_protect_interests
1	0.02	right_to_protection
1	0.02	right_to_religion
1	0.02	right_to_represenation
1	0.02	right_to_representation
1	0.02	right_to_speak_out
1	0.02	right_to_speak_out_against_something_one_doesn’t_agree_with
1	0.02	right_to_survive
1	0.02	right_to_the_pursuit_of_happiness
1	0.02	right_to_think_for_yourself
1	0.02	right_to_trial_by_peers
1	0.02	right_to_weapons
1	0.02	righteous
1	0.02	righteousness
1	0.02	rights_are_self_evident_and_come_from_god
1	0.02	rights_are_unalienable
1	0.02	rights_as_citizen
1	0.02	rights_of_people
1	0.02	rights_to_arms
1	0.02	rights_to_assemble
1	0.02	rights_to_free_speech
1	0.02	rule_of_law_is_one_of_the_the_u.s_stands
1	0.02	rules
1	0.02	rules_of_law
1	0.02	safe_food_and_water
1	0.02	safe_from_tyranny
1	0.02	safety_and_security
1	0.02	safety_for_citizens
1	0.02	safety_from_enemies
1	0.02	safety_provided_by_government
1	0.02	sanctity
1	0.02	sanctity_of_life
1	0.02	search_and_seizure
1	0.02	search_for_happiness
1	0.02	second_amendment
1	0.02	secrecy
1	0.02	secular_government
1	0.02	selective_service
1	0.02	self_accountability
1	0.02	self_defence
1	0.02	self_direction
1	0.02	self_goverment
1	0.02	self_incrimination
1	0.02	self_independence
1	0.02	self_indulgence
1	0.02	self_responsibility
1	0.02	self_rule
1	0.02	self_sufficency
1	0.02	self_sufficent
1	0.02	self_sufficiency
1	0.02	selfishness
1	0.02	senority
1	0.02	sense_of_belonging
1	0.02	separate_church/state
1	0.02	separation_church_and_state
1	0.02	separation_of_powers_in_government
1	0.02	separation_of_powers_to_balance_authority
1	0.02	separation_of_religion_and_state
1	0.02	serves_citizens
1	0.02	service_to_country
1	0.02	seventh_amendment
1	0.02	sexism
1	0.02	sincerity
1	0.02	slavery
1	0.02	smaller_government
1	0.02	soceity
1	0.02	social_class
1	0.02	social_equality
1	0.02	social_justice
1	0.02	socialistic
1	0.02	society
1	0.02	solidarity
1	0.02	somewhere_safe_for_everyone_to_come_if_they_need_refuge
1	0.02	sovereign
1	0.02	sovereign_interests
1	0.02	sovereign_nation
1	0.02	sovereignty_(spelling?)
1	0.02	sovererign
1	0.02	soveriegn
1	0.02	sovreignty
1	0.02	speech_expression
1	0.02	speedy_trial
1	0.02	spontaneous
1	0.02	spread_of_power
1	0.02	spyware
1	0.02	stand_for_freedom
1	0.02	standing_in_unity
1	0.02	stands_for_freedom
1	0.02	starting_over
1	0.02	state_rights
1	0.02	state_rights.
1	0.02	states_rights_vs_federal
1	0.02	states_rule_themselves
1	0.02	status_quo
1	0.02	straight_forward
1	0.02	strenght
1	0.02	strength_in_diversity
1	0.02	strength_through_unity
1	0.02	strong
1	0.02	strong_justice_system
1	0.02	strong_self_defense
1	0.02	stronger_community
1	0.02	structured
1	0.02	successful
1	0.02	super_power
1	0.02	superiority_complex
1	0.02	support
1	0.02	support_for_others
1	0.02	support_for_safety
1	0.02	support_for_those_in_need
1	0.02	taking_care_of_the_poor
1	0.02	the_1%
1	0.02	the_ability__to_travel_across_the_country
1	0.02	the_ability_to_choose
1	0.02	the_ability_to_vote_as_we_choose
1	0.02	the_action_to_vote_for_our_leaders
1	0.02	the_belief_in_god
1	0.02	the_constrint_of_federal_government
1	0.02	the_country_has_regressed.
1	0.02	the_first_amendment
1	0.02	the_freedom_of_speech
1	0.02	the_future
1	0.02	the_government_is_for_the_people
1	0.02	the_idea_that_elected_officials_govern_on_our_behalf
1	0.02	the_idea_that_we_are_striving_to_be_a_more_perfect_union
1	0.02	the_idea_that_we_have_in_our_constitution_a_living_document_that_can_be_amended_to_give_us_more_freedom_not_less
1	0.02	the_importance_of_the_individual
1	0.02	the_opportunity_for_happiness
1	0.02	the_protection_against_the_government
1	0.02	the_protection_of_the_united_states_military
1	0.02	the_pursuit_happiness
1	0.02	the_right_has_no_values
1	0.02	the_right_to_bare_arms
1	0.02	the_right_to_be_free
1	0.02	the_right_to_be_represented
1	0.02	the_right_to_freedom_of_religion
1	0.02	the_right_to_freedom_of_speech
1	0.02	the_right_to_liberty
1	0.02	the_right_to_life
1	0.02	the_right_to_live_where_wants_to
1	0.02	the_right_to_open_and_fair_elections
1	0.02	the_right_to_our_own_religion
1	0.02	the_right_to_own_arms
1	0.02	the_right_to_petition
1	0.02	the_right_to_privacy
1	0.02	the_right_to_pursue_happiness
1	0.02	the_right_to_start_a_business
1	0.02	the_right_to_trial_by_jury
1	0.02	the_right_to_vote
1	0.02	the_second_amendment
1	0.02	the_status_quo
1	0.02	the_stock_market
1	0.02	the_truth
1	0.02	the_u.s._sands_for_unity
1	0.02	the_u.s._stands_for_diversity
1	0.02	the_u.s._stands_for_equality
1	0.02	the_u.s._stands_for_individualism
1	0.02	the_u.s._stands_for_liberation
1	0.02	the_u.s_stands_for_opportunities_for_many_people_from_diverse_backgrounds.
1	0.02	the_value_of_democracy
1	0.02	the_value_of_liberty
1	0.02	the_value_of_the_individual
1	0.02	there_will_be_fair_and_free_elections.
1	0.02	they_create_problems
1	0.02	they_don’t_value_the_poor
1	0.02	they_value_others
1	0.02	they_value_the_rich
1	0.02	they_value_themselves
1	0.02	this_country_has_lost_its_way
1	0.02	thought,
1	0.02	three_governmental_branches
1	0.02	three_stooled_government
1	0.02	time
1	0.02	to_accept_immigrants
1	0.02	to_openly_speak
1	0.02	to_own_arms
1	0.02	to_remain_silent
1	0.02	tolerant
1	0.02	toleration
1	0.02	total_freedom
1	0.02	transperancy
1	0.02	travel_freely
1	0.02	trial_by_jury
1	0.02	trial_by_one’s_peers
1	0.02	trust_in_government
1	0.02	u.s_accommodates_people_from_different_diversity.
1	0.02	u.s_stands_for_democracy_and_power.
1	0.02	u.s_stands_for_innovation_and_development
1	0.02	un
1	0.02	unalienable_rights
1	0.02	under_god
1	0.02	underpaid_working
1	0.02	union
1	0.02	united_people
1	0.02	universal_sufferage
1	0.02	unjity
1	0.02	unreasonable_search_&_seizure
1	0.02	unremovable_rights
1	0.02	unthinking_rabid_patriotism
1	0.02	uphold_rights
1	0.02	upholding_the_law.
1	0.02	us_states
1	0.02	utility
1	0.02	utopia_doesn’t_exists.
1	0.02	valor
1	0.02	value_in_equality
1	0.02	value_of_equality
1	0.02	value_of_human_life
1	0.02	value_of_independence
1	0.02	value_of_self_determination
1	0.02	value_of_work
1	0.02	value_others
1	0.02	values_under_god,_based_on_the_bible
1	0.02	virtue
1	0.02	virtuous
1	0.02	vitality
1	0.02	voting_rights_for_all.
1	0.02	voting_rights_for_citizens
1	0.02	we_also_believe_in_democracy
1	0.02	we_are_all_equal.
1	0.02	we_are_all_equal_under_the_eyes_of_god
1	0.02	we_are_allowed_to_bear_arms
1	0.02	we_are_endowed_by_a_creator
1	0.02	we_are_free_to_worship_and_speak
1	0.02	we_believe_in_liberty
1	0.02	we_have_the_right_to_bear_arms
1	0.02	we_have_the_right_to_life,_liberty_and_the_pursuit_of_happiness
1	0.02	we_should_all_be_treated_as_such.
1	0.02	we_value_justice
1	0.02	wealth_and_hardwork
1	0.02	wealthy
1	0.02	welcome_immigrants
1	0.02	welfare
1	0.02	welfare_for_all
1	0.02	welfare_of_all
1	0.02	well_meaning
1	0.02	white_supremecist_roots
1	0.02	willing_to_change
1	0.02	willingness_to_strive
1	0.02	wisdom
1	0.02	wokeism
1	0.02	women_can_vote
1	0.02	women_can_work
1	0.02	womens_right
1	0.02	womens_rights
1	0.02	working_hard
1	0.02	world_leader
1	0.02	world_melting_pot
1	0.02	wrath
1	0.02	zionism

US in practice

df_bsc_long %>%
  left_join(df_bsc_elg %>% 
              dplyr::select(PID) %>% 
              mutate(elg = 1),by = "PID") %>% 
  filter(!is.na(elg)) %>% 
  dplyr::select(type,value) %>%
  filter(type == "uspractice") %>% 
  group_by(value) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(N)) %>% 
  dplyr::select(N,Perc,value) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                position = "left") %>% 
  scroll_box(width = "100%", height = "400px")

N	Perc	value
476	8.01	freedom
252	4.24	equality
229	3.86	democracy
149	2.51	freedom_of_speech
149	2.51	liberty
145	2.44	justice
122	2.05	capitalism
111	1.87	opportunity
97	1.63	diversity
92	1.55	individualism
78	1.31	independence
73	1.23	freedom_of_religion
67	1.13	power
56	0.94	fairness
48	0.81	greed
45	0.76	money
40	0.67	control
40	0.67	innovation
40	0.67	patriotism
39	0.66	free_speech
37	0.62	unity
34	0.57	pursuit_of_happiness
32	0.54	hard_work
32	0.54	wealth
29	0.49	religious_freedom
28	0.47	family
27	0.45	prosperity
26	0.44	strength
25	0.42	right_to_bear_arms
23	0.39	privacy
22	0.37	human_rights
21	0.35	religion
21	0.35	right_to_vote
20	0.34	individuality
19	0.32	happiness
19	0.32	integrity
19	0.32	materialism
18	0.30	honesty
18	0.30	security
16	0.27	corruption
16	0.27	education
16	0.27	equal_rights
16	0.27	leadership
16	0.27	loyalty
16	0.27	protection
16	0.27	safety
15	0.25	equal_opportunity
15	0.25	justice_for_all
15	0.25	self_reliance
14	0.24	equality_for_all
14	0.24	racism
14	0.24	rule_of_law
13	0.22	freedom_of_choice
13	0.22	success
13	0.22	war
12	0.20	choice
12	0.20	freedom_of_expression
12	0.20	progress
11	0.19	nationalism
11	0.19	rights
11	0.19	tolerance
11	0.19	truth
10	0.17	dominance
10	0.17	individual_freedom
10	0.17	life
10	0.17	peace
10	0.17	pride
10	0.17	voting_rights
9	0.15	autonomy
9	0.15	business
9	0.15	competition
9	0.15	individual_rights
9	0.15	inequality
9	0.15	love
9	0.15	military_strength
8	0.13	consumerism
8	0.13	imperialism
8	0.13	liberty_for_all
8	0.13	profit
8	0.13	respect
8	0.13	selfishness
8	0.13	work
7	0.12	acceptance
7	0.12	change
7	0.12	civil_rights
7	0.12	due_process
7	0.12	economic_opportunity
7	0.12	equity
7	0.12	free_market
7	0.12	freedom_for_all
7	0.12	generosity
7	0.12	honor
7	0.12	independance
7	0.12	self_determination
7	0.12	self_government
6	0.10	achievement
6	0.10	american_dream
6	0.10	big_government
6	0.10	compassion
6	0.10	defense
6	0.10	faith
6	0.10	global_leadership
6	0.10	guns
6	0.10	hope
6	0.10	immorality
6	0.10	individual_liberty
6	0.10	land_of_opportunity
6	0.10	manipulation
6	0.10	opportunities
6	0.10	oppression
6	0.10	representative_government
6	0.10	socialism
5	0.08	arrogance
5	0.08	bravery
5	0.08	charity
5	0.08	christianity
5	0.08	discrimination
5	0.08	entrepreneurship
5	0.08	equal_justice
5	0.08	ethics
5	0.08	free_elections
5	0.08	freedom_to_bear_arms
5	0.08	freedom_to_vote
5	0.08	god
5	0.08	growth
5	0.08	gun_rights
5	0.08	hardworking
5	0.08	immigration
5	0.08	inclusion
5	0.08	influence
5	0.08	kindness
5	0.08	military
5	0.08	military_might
5	0.08	na
5	0.08	right_to_arms
5	0.08	right_to_privacy
5	0.08	speech
5	0.08	values
5	0.08	work_ethic
4	0.07	courage
4	0.07	democratic
4	0.07	determination
4	0.07	division
4	0.07	dreams
4	0.07	economy
4	0.07	exploitation
4	0.07	force
4	0.07	free_enterprise
4	0.07	freedom_of_movement
4	0.07	freedom_of_press
4	0.07	freedom_to_protest
4	0.07	hegemony
4	0.07	humanity
4	0.07	hypocrisy
4	0.07	inclusivity
4	0.07	industry
4	0.07	injustice
4	0.07	law_and_order
4	0.07	laws
4	0.07	national_security
4	0.07	openness
4	0.07	opportunity_for_all
4	0.07	order
4	0.07	personal_responsibility
4	0.07	property_rights
4	0.07	resilience
4	0.07	right_to_protest
4	0.07	self_interest
4	0.07	social_justice
4	0.07	status_quo
4	0.07	strong_military
4	0.07	superiority
4	0.07	the_rich
4	0.07	the_right_to_bear_arms
4	0.07	trust
4	0.07	world_leader
3	0.05	ability_to_vote
3	0.05	ambition
3	0.05	assertiveness
3	0.05	authority
3	0.05	bear_arms
3	0.05	benevolence
3	0.05	brotherhood
3	0.05	caring
3	0.05	christian_values
3	0.05	commerce
3	0.05	community
3	0.05	conservative
3	0.05	creativity
3	0.05	culture
3	0.05	deception
3	0.05	diverse
3	0.05	domination
3	0.05	economic_growth
3	0.05	economic_opportunities
3	0.05	economic_power
3	0.05	empathy
3	0.05	empire
3	0.05	entitlement
3	0.05	equality_of_opportunity
3	0.05	exceptionalism
3	0.05	fair
3	0.05	fair_elections
3	0.05	fair_representation
3	0.05	freedom_of_the_press
3	0.05	freedom_to_work
3	0.05	freedoms
3	0.05	fun
3	0.05	future
3	0.05	gluttony
3	0.05	government_control
3	0.05	gun_ownership
3	0.05	hardwork
3	0.05	hate
3	0.05	hatred
3	0.05	hierarchy
3	0.05	honest
3	0.05	illegal_immigration
3	0.05	immigrants
3	0.05	individual
3	0.05	justice_for_everyone
3	0.05	law
3	0.05	liberalism
3	0.05	making_money
3	0.05	militarism
3	0.05	misogyny
3	0.05	morals
3	0.05	nepotism
3	0.05	open_borders
3	0.05	oppurtunity
3	0.05	ownership
3	0.05	perseverance
3	0.05	private_property
3	0.05	proud
3	0.05	representative_democracy
3	0.05	righteousness
3	0.05	science
3	0.05	selfish
3	0.05	social_mobility
3	0.05	stands_for_freedom
3	0.05	support
3	0.05	the_people
3	0.05	the_right_to_vote
3	0.05	time
3	0.05	tradition
3	0.05	understanding
3	0.05	upward_mobility
3	0.05	voting
3	0.05	working
3	0.05	world_domination
2	0.03	ability_to_choose
2	0.03	abuse_of_power
2	0.03	access
2	0.03	affordable_healthcare
2	0.03	aiding_other_countries
2	0.03	altruism
2	0.03	assembly
2	0.03	bare_arms
2	0.03	being_fair
2	0.03	belief_in_god
2	0.03	bigotry
2	0.03	brave
2	0.03	capital
2	0.03	captialism
2	0.03	chance
2	0.03	checks_and_balances
2	0.03	christian
2	0.03	christian_nationalism
2	0.03	classism
2	0.03	commercialism
2	0.03	compassionate
2	0.03	consistency
2	0.03	corporate_greed
2	0.03	corporations
2	0.03	corporatism
2	0.03	cruelty
2	0.03	democarcy
2	0.03	democratic_government
2	0.03	directness
2	0.03	dishonesty
2	0.03	economic_superiority
2	0.03	economic_wealth
2	0.03	efficiency
2	0.03	elections
2	0.03	employment
2	0.03	enjoyment
2	0.03	enterprise
2	0.03	equal_opportunities
2	0.03	equal_protection
2	0.03	equality_under_the_law
2	0.03	exploiting_the_poor
2	0.03	fair_taxation
2	0.03	fair_trial
2	0.03	family_values
2	0.03	fear
2	0.03	food
2	0.03	free
2	0.03	free_press
2	0.03	free_trade
2	0.03	freedom_of_assembly
2	0.03	freedom_of_opinion
2	0.03	freedom_of_speach
2	0.03	freedom_to_choose
2	0.03	freedom_to_live
2	0.03	freedom_to_protect_yourself
2	0.03	freedom_to_travel
2	0.03	freedom_to_worship
2	0.03	global_dominance
2	0.03	globalism
2	0.03	good_economy
2	0.03	goodwill
2	0.03	great
2	0.03	hard_work_rewarded
2	0.03	hard_working
2	0.03	hedonism
2	0.03	helpfulness
2	0.03	helping_other_countries
2	0.03	helping_the_needy
2	0.03	honesty_and_integrity
2	0.03	hunger
2	0.03	income_inequality
2	0.03	individual_freedoms
2	0.03	irresponsibility
2	0.03	jewish_supremacy
2	0.03	justice_for_some
2	0.03	law_abiding
2	0.03	lawlessness
2	0.03	leader
2	0.03	lies
2	0.03	melting_pot
2	0.03	merit
2	0.03	meritocracy
2	0.03	might_makes_right
2	0.03	military_dominance
2	0.03	military_force
2	0.03	military_power
2	0.03	monopoly
2	0.03	morality
2	0.03	nil
2	0.03	no_discrimination
2	0.03	oligarchy
2	0.03	open_mindedness
2	0.03	openess
2	0.03	overspending
2	0.03	own_firearms
2	0.03	passion
2	0.03	patriot
2	0.03	patriotic
2	0.03	personal_freedom
2	0.03	personal_safety
2	0.03	pluralism
2	0.03	political_freedom
2	0.03	politics
2	0.03	poverty
2	0.03	pragmatism
2	0.03	profits_over_people
2	0.03	property
2	0.03	property_ownership
2	0.03	protect_its_citizens
2	0.03	protection_of_citizens
2	0.03	protest
2	0.03	quality_education
2	0.03	quality_of_life
2	0.03	racial_equality
2	0.03	religion_freedom
2	0.03	representation
2	0.03	republic
2	0.03	rich_get_richer
2	0.03	right_to_assemble
2	0.03	right_to_due_process
2	0.03	right_to_fair_trial
2	0.03	right_to_life
2	0.03	right_to_worship
2	0.03	safe
2	0.03	self
2	0.03	self_preservation
2	0.03	self_worth
2	0.03	separation_of_power
2	0.03	service
2	0.03	slavery
2	0.03	sovereignty
2	0.03	stability
2	0.03	states_rights
2	0.03	stealing
2	0.03	superpower
2	0.03	taxes
2	0.03	technological_advancement
2	0.03	technological_innovation
2	0.03	the_american_dream
2	0.03	themselves
2	0.03	traditionalism
2	0.03	unequal
2	0.03	unfair_practices
2	0.03	value
2	0.03	value_of_freedom
2	0.03	violence
2	0.03	voter_rights
2	0.03	votes
2	0.03	war_machine
2	0.03	warmongering
2	0.03	wealth_accumulation
2	0.03	weapons
2	0.03	welfare
2	0.03	world_leadership
2	0.03	world_power
2	0.03	xenophobia
1	0.02	“christianity”
1	0.02	“equality”
1	0.02	“freedom”
1	0.02	“helping_out”
1	0.02	“protecting”_our_boarders
1	0.02	1%
1	0.02	_dominating_military
1	0.02	_fair_trial
1	0.02	a_democratic_country
1	0.02	a_fair_justice_system
1	0.02	a_helping_hand
1	0.02	a_multicultural_society
1	0.02	a_new_life
1	0.02	a_safe_place_to_call_home
1	0.02	aamerican
1	0.02	abelism
1	0.02	abide
1	0.02	ability
1	0.02	ability_to_come_and_go_as_you_please
1	0.02	ability_to_compromise
1	0.02	ability_to_dream
1	0.02	ability_to_flourish
1	0.02	ability_to_think
1	0.02	abuse_of_money
1	0.02	acceptance_for_all
1	0.02	acceptance_of_immigrants
1	0.02	acceptance_of_people
1	0.02	acceptance_to_cultures
1	0.02	access_to_education
1	0.02	access_to_guns
1	0.02	accommodating
1	0.02	accomplishment
1	0.02	accountability
1	0.02	accumulating_wealth.
1	0.02	accumulation_of_wealth
1	0.02	acheivement_ahead
1	0.02	action
1	0.02	adaptability
1	0.02	advancement
1	0.02	advancement_in_industry
1	0.02	advertising
1	0.02	affordability
1	0.02	affordable_housing
1	0.02	against_oppression
1	0.02	aggression
1	0.02	aid
1	0.02	alienable_rights
1	0.02	all_are_equal
1	0.02	all_are_welcome
1	0.02	all_created_equal
1	0.02	all_equal
1	0.02	all_freedoms
1	0.02	all_have_a_voice
1	0.02	all_inclusive_americans
1	0.02	all_men_and_woman_are_created_equal
1	0.02	allegiance_to_the_constitution
1	0.02	allows_immigrant_assimilation
1	0.02	ally
1	0.02	also_justice
1	0.02	always_help
1	0.02	always_in_debt
1	0.02	america
1	0.02	america_first
1	0.02	america_stands_for_change
1	0.02	america_stands_for_equality
1	0.02	america_stands_for_individulaism
1	0.02	america_stands_for_progress
1	0.02	america_stands_for_self_determination
1	0.02	american
1	0.02	american_suoeriority
1	0.02	ample_food,_water,_shelter,_government_assistance_when_needed
1	0.02	an_open_market_economy
1	0.02	and_respect
1	0.02	antiracism
1	0.02	any_liberalism/progressiveness
1	0.02	anyone_can_live_here
1	0.02	anyone_not_rich_doesn’t_matter_to_the_government
1	0.02	appearance
1	0.02	appretion
1	0.02	arms_freedom
1	0.02	as_i_say
1	0.02	asserting_world_dominance
1	0.02	assistance
1	0.02	attacking_the_other_side
1	0.02	attempted_problem_solving
1	0.02	authoritarian
1	0.02	authoritarianism
1	0.02	avarice
1	0.02	backroom_politics
1	0.02	bad_economy
1	0.02	bad_health
1	0.02	balanced_power_of_government_branches
1	0.02	bald_eagles
1	0.02	be_the_best
1	0.02	become_rich
1	0.02	being_a_beacon_of_hope
1	0.02	being_a_spendthrift
1	0.02	being_able_to_build_a_quality_of_life_for_families
1	0.02	being_able_to_vote_on_government_officials
1	0.02	being_able_to_vote_on_laws
1	0.02	being_an_american
1	0.02	being_an_individual
1	0.02	being_assertive_and_direct_in_communication_and_action_e.g._honesty/transparency.
1	0.02	being_humorous
1	0.02	being_kind
1	0.02	being_quick
1	0.02	being_the_best
1	0.02	being_united
1	0.02	being_who_you_are
1	0.02	belief_in_god_and_his_son_jesus
1	0.02	belief_they’re_best
1	0.02	beliefs
1	0.02	benefitting_corporations
1	0.02	better_lives
1	0.02	biased_judicial_system
1	0.02	bible_back_in_public_schools
1	0.02	big_profts
1	0.02	bill_of_rights
1	0.02	billionare’s_rights
1	0.02	black_people
1	0.02	blame
1	0.02	blind_patriotism/nationalism
1	0.02	bloat
1	0.02	bloated_federal_spending
1	0.02	bodily_autonomy
1	0.02	boldness
1	0.02	border_security
1	0.02	borderless_migrants
1	0.02	bottom_up_democracy
1	0.02	bragging
1	0.02	bribery
1	0.02	brutality
1	0.02	build_political_alliances_with_foreign_countries
1	0.02	build_through_taxes
1	0.02	bully
1	0.02	bullying
1	0.02	business_advances
1	0.02	business_growth
1	0.02	business_oppurtunity
1	0.02	business_ownership
1	0.02	buy_anything
1	0.02	camaraderie
1	0.02	cancel_culture
1	0.02	capitalism/money/greed
1	0.02	capitalism_and_individualism
1	0.02	capitalism_and_the_free_market_economy
1	0.02	capitalism_at_all_cost
1	0.02	capitalism_at_all_costs
1	0.02	capitalism_benefits_the_wealthy
1	0.02	capitalism_is_king
1	0.02	capitalist_democracy
1	0.02	capitalistic
1	0.02	capitalistic_actions
1	0.02	capitalistic_market
1	0.02	capitol_punishment
1	0.02	care
1	0.02	care_for_the_weak
1	0.02	care_for_vulnerable
1	0.02	career_oriented
1	0.02	caring_for_citizens
1	0.02	caring_for_others
1	0.02	cash
1	0.02	causing_war
1	0.02	censor_speech_that_puts_a_bad_light_on_the_administration_in_power
1	0.02	censorship
1	0.02	center_of_capitalism
1	0.02	chance_for_improvement
1	0.02	change_for_a_better_future
1	0.02	cheating
1	0.02	cheating_is_ok
1	0.02	checks_&_balances
1	0.02	checks_and_balances_on_executive_power
1	0.02	checks_on_power
1	0.02	child_labor
1	0.02	choices
1	0.02	christian_theocracy
1	0.02	christian_vlaues
1	0.02	christo_fascist_nation
1	0.02	church
1	0.02	church/state_separation
1	0.02	citizen_equality
1	0.02	citizen_rights
1	0.02	citizen_safety
1	0.02	citizen_vote
1	0.02	citizens_don’t_matter
1	0.02	citizenship
1	0.02	civil_liberty
1	0.02	civilian_control__military
1	0.02	civility
1	0.02	class_society
1	0.02	class_system
1	0.02	classes
1	0.02	classicism
1	0.02	classism_through_wealth
1	0.02	clear_communication
1	0.02	coercive
1	0.02	collaboration
1	0.02	colonialism
1	0.02	colonization_(“poor”/“defenseless”)
1	0.02	common_decency
1	0.02	community:strength_in_unity_and_support
1	0.02	compassion_for_others
1	0.02	compassion_for_vulnerable
1	0.02	competence
1	0.02	competitive
1	0.02	competitiveness
1	0.02	complaining
1	0.02	complete_racial_justice
1	0.02	compromise
1	0.02	comradere
1	0.02	concern_for_ourselves_above_others
1	0.02	confidence
1	0.02	conflict
1	0.02	conformist
1	0.02	conformity
1	0.02	confusion
1	0.02	connectedness
1	0.02	conniving
1	0.02	conquest
1	0.02	conquests
1	0.02	conservation
1	0.02	conservativism
1	0.02	conspicuous_consumption
1	0.02	constituent_representation
1	0.02	constitution
1	0.02	constitutional_government
1	0.02	contributing_the_lion’s_share_of_negative_impact_on_climate_change
1	0.02	control_life
1	0.02	control_of_citizens
1	0.02	control_of_commerce
1	0.02	control_of_others
1	0.02	control_of_people
1	0.02	control_of_the_people
1	0.02	control_of_women
1	0.02	control_over_all
1	0.02	control_over_people
1	0.02	control_religious_freedom
1	0.02	control_speech
1	0.02	controlled_government
1	0.02	controlled_growth
1	0.02	controlling_government
1	0.02	controlling_towards_women
1	0.02	convience
1	0.02	cooperation
1	0.02	coporations_over_individuals
1	0.02	cordial
1	0.02	corporate_freedom
1	0.02	corporate_interest_war
1	0.02	corporate_interests
1	0.02	corporate_owned
1	0.02	corporate_profits
1	0.02	corporate_representation
1	0.02	corporation
1	0.02	corporations_are_people
1	0.02	corporatization
1	0.02	corporatracy_gilded-elitists
1	0.02	corrupt
1	0.02	corrupt_government
1	0.02	corruption_of_children
1	0.02	coruption
1	0.02	create_a_family
1	0.02	create_things
1	0.02	credibility
1	0.02	creed
1	0.02	criminals_get_away_with_crimes
1	0.02	crony_capitalism
1	0.02	cultural
1	0.02	cultural_diversity
1	0.02	cultural_dominance
1	0.02	cultural_pot
1	0.02	customs
1	0.02	debt_ceiling_relief
1	0.02	debt_slavery
1	0.02	deceit
1	0.02	decency_still_exists
1	0.02	deciding_world_economy
1	0.02	decision_making_freedom
1	0.02	declining_competence_and_increasing_victimhood.
1	0.02	defeat_others
1	0.02	defend_property
1	0.02	defend_the_constitution
1	0.02	defender
1	0.02	defender_of_freedom
1	0.02	defender_of_the_world
1	0.02	defending_her_principles
1	0.02	defending_others
1	0.02	defending_our_country
1	0.02	defending_your_beliefs
1	0.02	defense_of_interest
1	0.02	degeneracy_uber_alles
1	0.02	degradation_of_morals
1	0.02	dei_and_lgbtq
1	0.02	delegative
1	0.02	deligence
1	0.02	democracry
1	0.02	democracy__universal_suffrage_of_citizens
1	0.02	democracy_for_all
1	0.02	democracy_for_all_people
1	0.02	democracy_for_everyone
1	0.02	democracy_for_people
1	0.02	democracy_is_an_important_value
1	0.02	democracy_is_one_of_the_value_that_u.s._stands_for
1	0.02	democracy_should_spread_everywhere
1	0.02	democracy_that_governs
1	0.02	democracy_voting
1	0.02	democracy_when_convenient
1	0.02	democrarcy
1	0.02	democratic_consensus
1	0.02	democratic_elections
1	0.02	democratic_process
1	0.02	democratic_republic
1	0.02	democratic_voting
1	0.02	democratic_voting_process
1	0.02	democratically_elected_representatives
1	0.02	demoracy
1	0.02	dependence
1	0.02	depressing
1	0.02	desire_to_continue_our_societal_norms
1	0.02	desire_to_standout
1	0.02	destiny
1	0.02	destruction_of_the_planet
1	0.02	different_races
1	0.02	dignity
1	0.02	diligence
1	0.02	diplomacy
1	0.02	diplomatic_solutions
1	0.02	dirty_politics
1	0.02	discipline
1	0.02	discoverer_and_inventor
1	0.02	discrimination_of_minorities
1	0.02	dismissal_of_the_poor
1	0.02	disregard
1	0.02	disrespect
1	0.02	diversity:_embracing_differences_enriches_society.
1	0.02	diversity_amongst_us
1	0.02	diversity_of_beliefs
1	0.02	divisive
1	0.02	divisiveness
1	0.02	do_just_enough_to_keep_people_satisfied.
1	0.02	do_not_judge
1	0.02	doing_what_you_want
1	0.02	domestic_and_foreign_relations
1	0.02	dominance_over_other_countries
1	0.02	domination_of_world_power
1	0.02	don’t_pay_attention_at_all_to_anyone_who_is_not_wealthy_unless_they_can_vote_for_you.
1	0.02	donor’s_bottom_lines
1	0.02	dont_know
1	0.02	dream
1	0.02	dreams_of_wealth
1	0.02	drive_to_be_“first”,_prestigeous
1	0.02	driven
1	0.02	drug
1	0.02	drug_abuse
1	0.02	economic_dominance_and_superiority
1	0.02	economic_freedom
1	0.02	economic_freedom_for_all
1	0.02	economic_gain
1	0.02	economic_inequality
1	0.02	economic_liberty
1	0.02	economic_mobility
1	0.02	economic_prosperity
1	0.02	economic_prowess
1	0.02	economic_stength
1	0.02	economic_strength
1	0.02	economic_stress
1	0.02	economic_superiority_and_success
1	0.02	economical_superiority
1	0.02	education_isn’t_a_guarantee_of_success
1	0.02	educational_opportunity
1	0.02	effort
1	0.02	ego
1	0.02	elder_abuse
1	0.02	elected_officials
1	0.02	electing_officials
1	0.02	elevation_of_the_rich
1	0.02	elimination_of_the_people
1	0.02	elite_exception
1	0.02	elites
1	0.02	elitism_in_government
1	0.02	elusive_opportunities
1	0.02	emotional_decisions
1	0.02	empathy/being_humane
1	0.02	encouragement
1	0.02	encouragement_to_all_citizens_to_rise_above_themselves
1	0.02	encroaching_government
1	0.02	endless_war
1	0.02	enforcing_laws
1	0.02	enforcing_the_law
1	0.02	enlightened_self_interest
1	0.02	enrich_the_wealthy
1	0.02	enrichment
1	0.02	entitlement_programs
1	0.02	entrenched_bureaucracy
1	0.02	entrepeneurship
1	0.02	entrepenurship
1	0.02	entrepreneurship_is_welcomed_here
1	0.02	environmental_protection
1	0.02	eqaul_rights
1	0.02	equaility
1	0.02	equal
1	0.02	equal_chances_for_all
1	0.02	equal_economy
1	0.02	equal_education
1	0.02	equal_justice_for_all
1	0.02	equal_oppertunity
1	0.02	equal_opportunity_and_prosperity
1	0.02	equal_opporunity
1	0.02	equal_oppritunities
1	0.02	equal_oppurtunity
1	0.02	equal_pay
1	0.02	equal_rights_for_all
1	0.02	equal_treatment
1	0.02	equal_vote_access
1	0.02	equality_among_all_citizens
1	0.02	equality_among_all_people
1	0.02	equality_among_people_(though_people_argue_there_is_not..)
1	0.02	equality_and_same_self_worth
1	0.02	equality_before_law
1	0.02	equality_before_the_law
1	0.02	equality_between_everyone_regardless_of_race_or_gender.
1	0.02	equality_between_other
1	0.02	equality_for_all_people
1	0.02	equality_for_all_persons.
1	0.02	equality_for_alll
1	0.02	equality_for_everyone
1	0.02	equality_for_people
1	0.02	equality_for_some
1	0.02	equality_in_theory
1	0.02	equality_is_one_of_the_u.s_values
1	0.02	equality_of_all
1	0.02	equality_of_all_human
1	0.02	equality_of_all_people
1	0.02	equality_of_citizens
1	0.02	equality_of_individuals
1	0.02	equalize_wealth
1	0.02	equally_representative_government
1	0.02	equity_(not_equality)
1	0.02	equity_versus_equality
1	0.02	establishment_elite_above_all
1	0.02	ethical
1	0.02	euality
1	0.02	every_citizen_has_a_voice_in_the_governing
1	0.02	every_person_is_equal
1	0.02	everyone_has_own_opinions
1	0.02	everyone_has_the_right_to_earn
1	0.02	everyone_is_equal
1	0.02	everyone_the_same
1	0.02	evil
1	0.02	excellence
1	0.02	excess
1	0.02	excessive_nationalism
1	0.02	exclusion
1	0.02	excusing_lucrative_malfeasance.
1	0.02	exercising_power
1	0.02	expansive_govenrment
1	0.02	expedient_trials
1	0.02	expensive_healthcare
1	0.02	exploitative_caplitalism
1	0.02	expression
1	0.02	expression_of_individualism
1	0.02	extortion
1	0.02	extremism
1	0.02	extremists
1	0.02	factual_communication
1	0.02	fail_elections
1	0.02	fair_and_equitable_justice
1	0.02	fair_courts
1	0.02	fair_dealing
1	0.02	fair_economic_conditions
1	0.02	fair_justice
1	0.02	fair_laws_and_judges
1	0.02	fair_treatment
1	0.02	fair_treatment_for_all_
1	0.02	fair_treatment_to_all
1	0.02	fair_voting
1	0.02	fair_wages
1	0.02	fairly_elected_representatives
1	0.02	fairness_for_all
1	0.02	fairness_in_dealing_with_people
1	0.02	fairness_to_everyone
1	0.02	false_decency
1	0.02	false_morals
1	0.02	false_priorities
1	0.02	false_sense_of_pride
1	0.02	fame
1	0.02	family_tradition
1	0.02	family_unity
1	0.02	fascism
1	0.02	favor
1	0.02	favoring_of_minorities
1	0.02	favoritism_for_corporations
1	0.02	favoritism_for_rich
1	0.02	favors_for_rich
1	0.02	favors_rich
1	0.02	fear_mongering
1	0.02	federal_power
1	0.02	feedom_for_all
1	0.02	feelings
1	0.02	feminist_man_hate
1	0.02	fight_for_freedom
1	0.02	fighters
1	0.02	fighting
1	0.02	fighting_against_tyranny
1	0.02	finances
1	0.02	financial
1	0.02	financial_gain
1	0.02	financial_stability
1	0.02	first_amendment_protections
1	0.02	first_amendment_rights
1	0.02	following_the_law
1	0.02	fondness
1	0.02	food_on_tables
1	0.02	for_the_people
1	0.02	foreign_aid
1	0.02	foreign_intervention
1	0.02	foreign_investment
1	0.02	forever_war_somewhere
1	0.02	forked_tongue
1	0.02	fredom_of_religion
1	0.02	free_and_fair
1	0.02	free_and_fair_elections_will_take_place
1	0.02	free_capital_market
1	0.02	free_choice
1	0.02	free_education
1	0.02	free_for_all
1	0.02	free_gatherings
1	0.02	free_market_capitalism
1	0.02	free_market_economy
1	0.02	free_markets
1	0.02	free_religion_choice
1	0.02	free_religon
1	0.02	free_speach
1	0.02	free_spirit
1	0.02	free_stuff
1	0.02	free_to_bear_arms
1	0.02	free_to_express_self
1	0.02	free_to_make_money
1	0.02	free_to_vote
1	0.02	free_will
1	0.02	freedom,_overall
1	0.02	freedom_and_deliverance_of_its_people
1	0.02	freedom_and_democratic_process.
1	0.02	freedom_and_equality
1	0.02	freedom_and_order
1	0.02	freedom_for_all_people
1	0.02	freedom_for_most
1	0.02	freedom_for_people
1	0.02	freedom_for_wealthy
1	0.02	freedom_from_discrimination
1	0.02	freedom_from_harm
1	0.02	freedom_from_oppression
1	0.02	freedom_from_predjuice
1	0.02	freedom_from_religion
1	0.02	freedom_from_searchs
1	0.02	freedom_from_tyranny
1	0.02	freedom_from_tyrany
1	0.02	freedom_in_general
1	0.02	freedom_is_paramount,_but_with_guardrails_that_protect_and_empower_people
1	0.02	freedom_is_part_of_what_u.s_stands_for
1	0.02	freedom_mostly
1	0.02	freedom_of/from_religion
1	0.02	freedom_of_action
1	0.02	freedom_of_action_as_long_as_legal
1	0.02	freedom_of_all
1	0.02	freedom_of_and_from_religion
1	0.02	freedom_of_belief
1	0.02	freedom_of_beliefs
1	0.02	freedom_of_education
1	0.02	freedom_of_employment
1	0.02	freedom_of_entrepreneurship
1	0.02	freedom_of_expression.
1	0.02	freedom_of_ideology/religion
1	0.02	freedom_of_ownership
1	0.02	freedom_of_religion_is_vital
1	0.02	freedom_of_religon
1	0.02	freedom_of_speech_and_association
1	0.02	freedom_of_speech_and_expression
1	0.02	freedom_of_speech_and_press.
1	0.02	freedom_of_speech_more_or_less_but_it’s_becoming_less
1	0.02	freedom_of_taxation_without_representation
1	0.02	freedom_of_thought
1	0.02	freedom_of_travel
1	0.02	freedom_of_tyranny
1	0.02	freedom_of_work
1	0.02	freedom_of_worship
1	0.02	freedom_off_religion
1	0.02	freedom_off_speech
1	0.02	freedom_suppression
1	0.02	freedom_to_a_fair_trial
1	0.02	freedom_to_achieve
1	0.02	freedom_to_achieve_goals
1	0.02	freedom_to_assemble
1	0.02	freedom_to_assembly
1	0.02	freedom_to_be_hateful
1	0.02	freedom_to_be_massive_hypocrites
1	0.02	freedom_to_be_who_you_are_truely
1	0.02	freedom_to_be_yourself
1	0.02	freedom_to_choose_abortion
1	0.02	freedom_to_choose_where_to_live
1	0.02	freedom_to_criticize_anyone_you_want
1	0.02	freedom_to_decide
1	0.02	freedom_to_defend_one_self_from_danger
1	0.02	freedom_to_defend_yourself
1	0.02	freedom_to_do_whatever_work_you_want
1	0.02	freedom_to_kill_kids_with_guns_in_schools
1	0.02	freedom_to_learn
1	0.02	freedom_to_live_how_you_want_to_live
1	0.02	freedom_to_love
1	0.02	freedom_to_make_as_much_money_as_u_want_to
1	0.02	freedom_to_move_about
1	0.02	freedom_to_not_follow_anything_we_actually_stand_for
1	0.02	freedom_to_obtain_a_firearm
1	0.02	freedom_to_own_a_weapon
1	0.02	freedom_to_shoot_guns
1	0.02	freedom_to_speak_your_mind
1	0.02	freedom_to_start_a_business
1	0.02	freedom_to_succeed
1	0.02	freedom_to_think
1	0.02	freedom_to_work_and_make_money
1	0.02	freedoms_of_religion,_speech
1	0.02	freendom_of_expression
1	0.02	fresh_start
1	0.02	friendliness
1	0.02	friendly
1	0.02	friendship
1	0.02	fundamental
1	0.02	fundamental_rights
1	0.02	funding_wars
1	0.02	furtherance_of_political_careers
1	0.02	gain
1	0.02	gain_power
1	0.02	gaslighting
1	0.02	gatekeeping
1	0.02	gender_discrimination
1	0.02	gender_equality
1	0.02	generating_wealth
1	0.02	generosity_to_others
1	0.02	gentrification_(“poor”/“defenseless”)
1	0.02	getting_rich
1	0.02	getting_their_way
1	0.02	globa_influence
1	0.02	global_assistance
1	0.02	global_domination
1	0.02	global_economy_influence
1	0.02	global_expansion_of_zionism
1	0.02	global_hegemony
1	0.02	global_influence
1	0.02	global_political_influence
1	0.02	global_power
1	0.02	global_warming_denial
1	0.02	global_warming_reduction
1	0.02	globalize_the_world_at_the_expense_of_country_autonomy
1	0.02	glory
1	0.02	god_fearing
1	0.02	god_given_rights
1	0.02	going_against_the_people
1	0.02	golden_rule
1	0.02	good_for_all
1	0.02	good_government
1	0.02	good_life
1	0.02	good_schools
1	0.02	goodness
1	0.02	gov_controls
1	0.02	govern_themselves
1	0.02	governance
1	0.02	government
1	0.02	government_by_the_people
1	0.02	government_dependence
1	0.02	government_given_rights
1	0.02	government_intervention
1	0.02	government_is_chosen_by_the_few_at_the_expense_of_the_many
1	0.02	government_is_democratic
1	0.02	government_power
1	0.02	government_representation
1	0.02	government_structure
1	0.02	greed_is_good.
1	0.02	greedy
1	0.02	greedy_people
1	0.02	grinding_hard_work_for_the_lower_classes
1	0.02	gun
1	0.02	gun_carrying
1	0.02	gun_control
1	0.02	handouts
1	0.02	hapiness
1	0.02	happiness_in_life
1	0.02	hard_work_is_good_but_may_not_pay_off_much
1	0.02	hard_workers
1	0.02	hatred_of_white_people
1	0.02	hatred_towards_weak
1	0.02	have_the_american_dream
1	0.02	having_diversity
1	0.02	having_equality
1	0.02	having_freedom
1	0.02	having_good_healthcare
1	0.02	having_self_government
1	0.02	having_the_chance_to_succeed
1	0.02	having_unity
1	0.02	health
1	0.02	health_and_education
1	0.02	health_and_happiness
1	0.02	healthcare
1	0.02	healthcare_for_everyone
1	0.02	healthy_economy
1	0.02	hegemonic
1	0.02	heirachy_based_system.
1	0.02	help_for_nations
1	0.02	help_the_citizen_achieve_their_full_potential
1	0.02	help_the_world
1	0.02	help_those_in_need
1	0.02	help_those_less_fortunate
1	0.02	helpfullness
1	0.02	helping
1	0.02	helping_businesses
1	0.02	helping_people
1	0.02	helping_the_downtrodden
1	0.02	helping_the_poor
1	0.02	heroism
1	0.02	high_insurance_costs
1	0.02	high_medical_costs
1	0.02	high_standards
1	0.02	high_taxes
1	0.02	higher_education
1	0.02	highest_vote_wins
1	0.02	history
1	0.02	home_ownership
1	0.02	homelessness
1	0.02	homosexual_imperialism
1	0.02	homosexuality
1	0.02	honest_elections
1	0.02	honest_government
1	0.02	honest_leadership
1	0.02	honestly
1	0.02	honesty_and_loyalty
1	0.02	honesty_and_truth
1	0.02	honor_the_rich
1	0.02	honoring_traditions
1	0.02	hosts_immigrants
1	0.02	housing
1	0.02	human_dignity
1	0.02	human_right
1	0.02	humane
1	0.02	humanitarian_
1	0.02	humanity_towards_individuals
1	0.02	humans_at_a_certain_stage_are_not_entitled_to_life.
1	0.02	humility
1	0.02	hustle
1	0.02	hypocrital
1	0.02	hypocriticism
1	0.02	i_think_of_freedom
1	0.02	ideals_of_powerful
1	0.02	identity_values.
1	0.02	idiocy
1	0.02	if_you’re_rich_you’ve_got_it_made
1	0.02	if_you_make_it_to_our_borders_we_will_let_you_in.
1	0.02	ignores_its_own_people
1	0.02	illegal_taxes
1	0.02	illusion_of_equality
1	0.02	impartial_equality
1	0.02	impartial_justice
1	0.02	impartial_treatment
1	0.02	implementing_rules_for_all
1	0.02	improvement
1	0.02	in_god_we_trust
1	0.02	in_short_supply
1	0.02	incarceration
1	0.02	inclusive
1	0.02	inclusiveness
1	0.02	inclusiveness_and_dignity
1	0.02	inclusiveness_and_diversity
1	0.02	inclusivility
1	0.02	inconsistent
1	0.02	independence_and_self_reliance
1	0.02	independence_and_self_sufficient
1	0.02	independence_from_others
1	0.02	independency
1	0.02	independent
1	0.02	indepenedence
1	0.02	indepenence_and_self_reliance
1	0.02	individual_autonomy
1	0.02	individual_freedom_more_or_less
1	0.02	individual_freedoms_while_taking_account_the_greater_good_of_all._
1	0.02	individual_has_rights
1	0.02	individual_liberties
1	0.02	individual_right
1	0.02	individual_rights,_freedom
1	0.02	individual_rights_a_person_has
1	0.02	individual_rightsequ
1	0.02	individualisim
1	0.02	individualism.
1	0.02	individualism_and_self_reliance.
1	0.02	individualism_is_an_important_part_of_u.s_value
1	0.02	individualism_to_pursue_goals
1	0.02	individualistic
1	0.02	individualization
1	0.02	individulaiity
1	0.02	individulism
1	0.02	indiviuality
1	0.02	indivuality
1	0.02	indivudualism
1	0.02	indoctrination
1	0.02	indpendence
1	0.02	industrialization
1	0.02	ineffective_government
1	0.02	inequal_application_of_the_law
1	0.02	inequality_among_citizens
1	0.02	inequality_of_citizens
1	0.02	inequality_of_resources
1	0.02	inequilty
1	0.02	inequity
1	0.02	inevitable_change
1	0.02	influencing_other_governments
1	0.02	informality
1	0.02	inhumane
1	0.02	innocent_until_proven
1	0.02	innocent_until_proven_guilty
1	0.02	innocent_until_proven_guilty,_the_right_to_question_your_accusers
1	0.02	innovation:_advancing_through_creativity_and_ingenuity
1	0.02	inovation
1	0.02	instant_gratification
1	0.02	institutionalized_racism
1	0.02	insurance_profits
1	0.02	intense_work
1	0.02	interference_and_destabilization_of_foreign_governments
1	0.02	internal_dissension
1	0.02	international_leadership
1	0.02	interventionism
1	0.02	interventionist
1	0.02	intimidation
1	0.02	intolerance_of_beliefs
1	0.02	isolationism
1	0.02	it_stand_for_equality
1	0.02	it_stand_for_liberty
1	0.02	jewish_people
1	0.02	jewish_power
1	0.02	job_choice
1	0.02	jobs
1	0.02	jobs_available
1	0.02	judgemental
1	0.02	judicial
1	0.02	justice.
1	0.02	justice_for_most
1	0.02	justice_for_people
1	0.02	justices
1	0.02	keep_a_good_pr_team.
1	0.02	keep_bear_arms
1	0.02	keep_people_down
1	0.02	keeping_peace_in_other_countries
1	0.02	keeping_the_poor_people_poor
1	0.02	keeping_the_rich_more_rich
1	0.02	labor_force
1	0.02	lack_of_enforcement
1	0.02	lack_of_freedom
1	0.02	laisse_faire_economic_system
1	0.02	land_of_diversity
1	0.02	land_of_dreams
1	0.02	law_&_order
1	0.02	law_due_process
1	0.02	lawfare
1	0.02	lawful
1	0.02	lawful_due_process
1	0.02	laws_are_only_for_certain_people_in_the_us
1	0.02	laws_for_everyone
1	0.02	laws_that_keep_citizens_safe
1	0.02	laws_that_protect_you_(though_lately…)
1	0.02	leaders_democratically_chosen
1	0.02	leading_the_world_in_a_high_standard_of_living
1	0.02	learning
1	0.02	legal_counsel
1	0.02	legal_equality
1	0.02	legal_immigration
1	0.02	legal_justice
1	0.02	legal_representation
1	0.02	legal_rights
1	0.02	less_government_rule
1	0.02	leverage
1	0.02	liberalism_(classic_definition)
1	0.02	liberation
1	0.02	liberty.
1	0.02	liberty_a_person_exemplifies
1	0.02	liberty_and_freedom
1	0.02	liberty_and_justice
1	0.02	liberty_of_living
1	0.02	licentiousness
1	0.02	lie_to_poor
1	0.02	lies_&_betrayal
1	0.02	life_sustaining
1	0.02	limit_speech_that_hurts_others
1	0.02	limited_censorship
1	0.02	limited_community_socioeconomic_supports
1	0.02	limited_fairness/equality
1	0.02	limited_global_collaboration
1	0.02	limited_government
1	0.02	limited_individual_rights
1	0.02	limited_power
1	0.02	limited_restraints
1	0.02	limiting_individual_freedom
1	0.02	litigation
1	0.02	live_anywhere
1	0.02	live_for_commerce
1	0.02	live_free
1	0.02	live_in_freedom
1	0.02	living_the_life_you_want
1	0.02	living_your_life_the_way_you_want
1	0.02	look_towards_the_future
1	0.02	lost_our_way
1	0.02	lost_vision
1	0.02	lots_of_family
1	0.02	lots_of_poverty
1	0.02	love_of_wars
1	0.02	loving
1	0.02	low_intelligence
1	0.02	loyal
1	0.02	lust
1	0.02	lying
1	0.02	lying,_cheating,stealing_and_profiting_from_government”service.”
1	0.02	maintain_51%_control.
1	0.02	maintaining_elitism
1	0.02	maintaining_power
1	0.02	majority_second_class
1	0.02	make_1%_richer
1	0.02	make_lobbyists_happy
1	0.02	make_money
1	0.02	make_more_money
1	0.02	make_own_rules
1	0.02	make_up_lies_against_the_people
1	0.02	make_whites_sovereign
1	0.02	making_it
1	0.02	making_it_easy_for_the_rich
1	0.02	making_it_hard_for_the_poor
1	0.02	making_sure_those_in_power_have_money_and_keep_their_power
1	0.02	male_dominance
1	0.02	manipulative
1	0.02	many_jobs
1	0.02	many_regulations
1	0.02	market_capitalism
1	0.02	market_economy
1	0.02	market_gains
1	0.02	marketing
1	0.02	marriage_equality
1	0.02	marry_your_choice
1	0.02	mass_media_propaganda
1	0.02	material_wealth
1	0.02	materialistic
1	0.02	materilism
1	0.02	me_first
1	0.02	media_is_controlled_by_the_few
1	0.02	medical_care
1	0.02	melting_pot_of_cultures
1	0.02	mercy
1	0.02	merit_based_rewards
1	0.02	military_(hard)_power
1	0.02	military_defence
1	0.02	military_discipline_and_success
1	0.02	military_industrial_complex
1	0.02	military_might_is_right
1	0.02	military_policing
1	0.02	military_presence
1	0.02	military_strength/might
1	0.02	military_supremacy
1	0.02	mine
1	0.02	miranda_rights
1	0.02	misery
1	0.02	misinformation_control
1	0.02	mobility
1	0.02	monetary_manipulation
1	0.02	money_above_all
1	0.02	money_equals_power
1	0.02	money_is_king
1	0.02	money_is_power
1	0.02	money_laundering
1	0.02	money_making
1	0.02	money_talks
1	0.02	monies_toward_ones_constituents
1	0.02	moral
1	0.02	moral_integrity
1	0.02	moralitt
1	0.02	morality_is_basis
1	0.02	most_advanced
1	0.02	mostly_free_speech
1	0.02	multiple_freedoms
1	0.02	nanny_state
1	0.02	narcissism
1	0.02	narcissism_is_rampant
1	0.02	nation_security
1	0.02	natural_protection
1	0.02	nature
1	0.02	neglect
1	0.02	negligence
1	0.02	no_ethnics_discrimination
1	0.02	no_funds
1	0.02	no_gender_discrimination
1	0.02	no_hate
1	0.02	no_help_for_the_homeless
1	0.02	no_illegal_searches
1	0.02	no_money
1	0.02	no_nobility
1	0.02	no_racial_discrimination
1	0.02	no_real_ownership
1	0.02	nobody_above_law
1	0.02	non_christian_values
1	0.02	non_discrimination
1	0.02	non_discrimination_equality
1	0.02	non_partisonship
1	0.02	non_racist
1	0.02	normality
1	0.02	nostalgia
1	0.02	not_being_offensive
1	0.02	not_fair_for_all
1	0.02	not_for_us
1	0.02	not_sure_really
1	0.02	not_that_great
1	0.02	nothing
1	0.02	obesity
1	0.02	occasional_hyprocrisy
1	0.02	oddities
1	0.02	old_views
1	0.02	one_nation
1	0.02	oneness
1	0.02	open
1	0.02	open_competition
1	0.02	open_decisions
1	0.02	open_economy
1	0.02	open_minded
1	0.02	open_society
1	0.02	open_to_different_cultures_and_people
1	0.02	open_to_different_ideas
1	0.02	open_to_many
1	0.02	open_voting
1	0.02	opportunism
1	0.02	opportunistic
1	0.02	opportunities_for_all
1	0.02	opportunity_
1	0.02	opportunity_for_all_people
1	0.02	opportunity_for_all_who_would_work_hard
1	0.02	opportunity_for_shelter
1	0.02	opportunity_for_success
1	0.02	opportunity_for_wealth
1	0.02	opportunity_in_life
1	0.02	opportunity_is_an_important_part_of_u.s_values
1	0.02	opportunity_to_succeed
1	0.02	opportuniy
1	0.02	opporturnity
1	0.02	oppressing_conservatives
1	0.02	oppression_of_minorities
1	0.02	opression_of_minorities
1	0.02	opression_of_the_poor
1	0.02	optimism_and_belief_in_the_american_dream.
1	0.02	options
1	0.02	other_countries
1	0.02	our_country_allows_our_congress_to_do_inside_trading
1	0.02	our_country_is_a_republic
1	0.02	our_country_is_best
1	0.02	our_country_is_corrupt
1	0.02	our_country_lies
1	0.02	our_rights_are_inalienable_and_come_from_a_creator
1	0.02	outspokenness
1	0.02	over_acceptance
1	0.02	over_taxation
1	0.02	over_taxing
1	0.02	overbearing
1	0.02	overbearing_government
1	0.02	overly_expensive_healthcare
1	0.02	overworked
1	0.02	own_a_gun
1	0.02	own_interests
1	0.02	partiotism
1	0.02	partisanship
1	0.02	party_loyalty
1	0.02	party_only_one_right
1	0.02	party_values
1	0.02	passionate
1	0.02	patriotism_is_another_great_value
1	0.02	patriotism_to_our_standards
1	0.02	peace_and_safety
1	0.02	peaceful_discourse
1	0.02	peacekeeper
1	0.02	people_are_allowed_to_go_without_food
1	0.02	perception_as_caring
1	0.02	perpetual_war
1	0.02	persecution_of_whistleblowers
1	0.02	personal_expression
1	0.02	personal_freedoms
1	0.02	personal_freedoms_and_rights
1	0.02	personal_interests
1	0.02	personal_liberty
1	0.02	personal_religion
1	0.02	personal_rights
1	0.02	persuit
1	0.02	policies
1	0.02	political_correctness
1	0.02	political_corruption
1	0.02	political_dissent
1	0.02	political_division
1	0.02	political_influence
1	0.02	political_parties_will_not_compromise_for_the_better_of_the_people
1	0.02	political_power
1	0.02	politicians_are_out_for_themselves
1	0.02	politics_over_statesmanship
1	0.02	pompous
1	0.02	poor
1	0.02	poor_healthcare
1	0.02	poor_leaders
1	0.02	poor_leadership
1	0.02	popularity
1	0.02	positive_perception
1	0.02	possession_of_firearms
1	0.02	postliberalism
1	0.02	potential
1	0.02	potential_theocracy
1	0.02	power_for_the_rich
1	0.02	power_in_government
1	0.02	power_on_the_world_stage
1	0.02	power_over_all
1	0.02	powerful
1	0.02	practicality
1	0.02	practice_any_religion
1	0.02	practice_different_religion
1	0.02	pragmatic_legislation
1	0.02	prayer
1	0.02	prejudice
1	0.02	preserve_itself
1	0.02	press
1	0.02	prestige
1	0.02	presumed_innocence
1	0.02	pretend_caring
1	0.02	prey_on_weak
1	0.02	principles
1	0.02	printing_excess_currency
1	0.02	pro_business
1	0.02	pro_democracy
1	0.02	pro_minority
1	0.02	productive
1	0.02	productiveness
1	0.02	profit_over_people
1	0.02	profitablity
1	0.02	profiteering_politicians
1	0.02	progress_going_forward
1	0.02	projecting_hubristic_superiority.
1	0.02	promise
1	0.02	promoting_criminality
1	0.02	promotion_of_socialism
1	0.02	proper
1	0.02	property_owner’s_rights
1	0.02	property_ownership_is_still_encouraged
1	0.02	propogation_of_centralised_power
1	0.02	protect_its_allies
1	0.02	protect_our_flag
1	0.02	protect_the_1%
1	0.02	protect_the_rich
1	0.02	protect_the_united_states
1	0.02	protect_yourself
1	0.02	protecting_other_nations
1	0.02	protecting_others
1	0.02	protecting_our_own
1	0.02	protecting_politicians
1	0.02	protecting_status_quo
1	0.02	protecting_the_wealthy
1	0.02	protection_from_government
1	0.02	protection_from_unreasonable_search_and_seizure
1	0.02	protection_of_individual_rights
1	0.02	protection_of_innocent_babies_in_the_womb
1	0.02	protection_of_life
1	0.02	protection_of_oneself
1	0.02	protection_of_poor
1	0.02	protection_of_the_weak
1	0.02	provide_for_citizens
1	0.02	provides_for_common_defense
1	0.02	provides_our_liberty
1	0.02	public_rights
1	0.02	public_speech
1	0.02	punishing_old_and_young
1	0.02	pursue_of_happiness
1	0.02	pursuing_national_interests_abroad
1	0.02	pursuit_of_justice
1	0.02	pursuit_of_prosperity
1	0.02	pursuit_of_wealth
1	0.02	push_back_on_political_opponents
1	0.02	pushing_beliefs
1	0.02	pushing_people_away
1	0.02	race
1	0.02	race_equality
1	0.02	race_relations
1	0.02	racial_diversity
1	0.02	racial_inequality
1	0.02	racial_privilege
1	0.02	racially_ambiguous
1	0.02	racisim
1	0.02	racism_and_division
1	0.02	racism_as_law
1	0.02	racist
1	0.02	rags_to_riches
1	0.02	reach_for_wealth
1	0.02	really_really_sad
1	0.02	receiving_equal_justice
1	0.02	regulation_of_business
1	0.02	regulations
1	0.02	relgious_freedom
1	0.02	religious_belief
1	0.02	religious_choice
1	0.02	religious_freedoms
1	0.02	religious_intolerance
1	0.02	religious_liberty
1	0.02	religious_practice
1	0.02	religious_practice_freedom
1	0.02	religious_tolerance
1	0.02	religious_zealotry
1	0.02	religious_zealots
1	0.02	representation_in_government
1	0.02	repression_of_poor
1	0.02	republican_government
1	0.02	resilent
1	0.02	resiliance
1	0.02	resource_generation
1	0.02	respect_other_people
1	0.02	respectful_of_others
1	0.02	responsibility
1	0.02	restraint_of_freedom
1	0.02	restrict_speech
1	0.02	restricted_arms_ownership
1	0.02	restricted_freedoms
1	0.02	restricted_speech
1	0.02	reward_the_rich
1	0.02	rewarding_illegals
1	0.02	rewarding_the_rich
1	0.02	rich
1	0.02	rich_interests
1	0.02	rich_people_finish_first
1	0.02	rich_richer
1	0.02	rich_rules
1	0.02	rich_vs_poor_divide
1	0.02	right_a_fair_judicial_system
1	0.02	right_of_privacy
1	0.02	right_of_speech
1	0.02	right_pursuit_happiness
1	0.02	right_to_a_fair_trial
1	0.02	right_to_a_jury
1	0.02	right_to_a_voice
1	0.02	right_to_an_education
1	0.02	right_to_assemble/protest/vote_freely
1	0.02	right_to_bare_arms
1	0.02	right_to_be_born
1	0.02	right_to_bear
1	0.02	right_to_business
1	0.02	right_to_choose
1	0.02	right_to_defend_oneself
1	0.02	right_to_exist
1	0.02	right_to_express_oneself.
1	0.02	right_to_filibuster
1	0.02	right_to_free_speech
1	0.02	right_to_gerrymander
1	0.02	right_to_guns
1	0.02	right_to_live
1	0.02	right_to_move_freely
1	0.02	right_to_own
1	0.02	right_to_own_firearms
1	0.02	right_to_peaceably_assemble
1	0.02	right_to_peition
1	0.02	right_to_personal_autonomy
1	0.02	right_to_property
1	0.02	right_to_protection
1	0.02	right_to_pursue_any_occupation
1	0.02	right_to_speak
1	0.02	right_to_speak_freely_(write/listen_as_well)
1	0.02	right_to_speech
1	0.02	right_to_travel_the_us_freely
1	0.02	right_to_trial
1	0.02	right_to_work
1	0.02	right_to_worship_freely
1	0.02	right_to_worship_freely_and_openly
1	0.02	righteous
1	0.02	rights_for_rich
1	0.02	rights_for_wealthy
1	0.02	rights_of_expression
1	0.02	rights_of_life
1	0.02	rights_of_religion
1	0.02	rights_of_the_individual
1	0.02	rights_to_a_trial
1	0.02	rights_to_arm
1	0.02	rights_to_assemble
1	0.02	rights_to_practice_religion
1	0.02	rights_to_vote
1	0.02	rising_above
1	0.02	roof_over_head
1	0.02	rugged_individualism
1	0.02	rule_by_elites
1	0.02	rule_of_law/justice
1	0.02	rules
1	0.02	russia
1	0.02	sacrafice
1	0.02	safe_spaces
1	0.02	safety_and_security
1	0.02	safety_concerns
1	0.02	safety_for_all
1	0.02	safety_for_people
1	0.02	same_opportunities
1	0.02	saying_what_you_want
1	0.02	scams
1	0.02	scientific_advancement
1	0.02	scientific_research
1	0.02	second_amendment_upheld
1	0.02	secrecy
1	0.02	secret_deals
1	0.02	secrets
1	0.02	secular
1	0.02	secular_values
1	0.02	secularism
1	0.02	secure
1	0.02	security_and_safety
1	0.02	sel_governance
1	0.02	selective_freedom
1	0.02	self_aggrandisement
1	0.02	self_autonomy
1	0.02	self_care_values
1	0.02	self_centeredness
1	0.02	self_expression
1	0.02	self_flagellation
1	0.02	self_governed
1	0.02	self_protection
1	0.02	self_rights
1	0.02	self_sacrificial
1	0.02	self_sufficiency
1	0.02	selg_government
1	0.02	semi_freedom
1	0.02	sense_of_pride
1	0.02	separate_rich_from_poor
1	0.02	separation_and_division
1	0.02	separation_of_church/state
1	0.02	separation_of_church_and_state
1	0.02	seperation_of_church_and_state
1	0.02	serfdom
1	0.02	serving_the_wealthy
1	0.02	sex
1	0.02	sexism
1	0.02	sexual_freedom
1	0.02	sexual_inequality
1	0.02	shenanigans
1	0.02	shitty_medical_care
1	0.02	simple
1	0.02	slavery_via_imprisonment
1	0.02	slef_determination
1	0.02	slowing_progress
1	0.02	small_mindedness
1	0.02	smart
1	0.02	social_behavior
1	0.02	social_class
1	0.02	social_darwinism
1	0.02	social_justice_wokeness
1	0.02	social_media
1	0.02	social_media_censorship
1	0.02	social_programs
1	0.02	socialism_before_capitalism
1	0.02	socialized_medicine
1	0.02	societal_degradation
1	0.02	solidarity
1	0.02	some_equal_opportunity
1	0.02	some_freedom_of_religion
1	0.02	some_freedom_of_speech
1	0.02	sovereign_government
1	0.02	sovereignity
1	0.02	sovereingty
1	0.02	speaking_your_mind
1	0.02	speech_and_guns
1	0.02	speedy_trial
1	0.02	spontaneous
1	0.02	stand_for_freedom
1	0.02	standing_together_when_need_be
1	0.02	standing_united
1	0.02	standing_up_for_allies
1	0.02	standing_up_for_the_people
1	0.02	stands_for_equality
1	0.02	stands_for_innovation
1	0.02	stands_for_liberty
1	0.02	stands_for_opportunity
1	0.02	stands_for_peace
1	0.02	stands_for_rights
1	0.02	states’_rights
1	0.02	status
1	0.02	status_trumps_all
1	0.02	steadfastness
1	0.02	stepping_in_to_end_conflicy
1	0.02	stifling_christianity
1	0.02	stock_market_growth
1	0.02	stratification
1	0.02	strength_and_power
1	0.02	strength_in_diversity
1	0.02	strength_in_unity_and_support
1	0.02	strength_of_character
1	0.02	strength_of_country
1	0.02	strength_of_military
1	0.02	strive_for_equality
1	0.02	strive_for_success
1	0.02	striving
1	0.02	strong_army
1	0.02	strong_democratic_system
1	0.02	strong_economy
1	0.02	strong_federal_government
1	0.02	strong_justice_system
1	0.02	strong_unity
1	0.02	strong_values
1	0.02	strongest_military
1	0.02	subjugating_exploitable_societies.
1	0.02	subversion
1	0.02	sucess
1	0.02	super_power
1	0.02	superiority_of_government
1	0.02	support_allies
1	0.02	support_for_all
1	0.02	support_for_allies
1	0.02	support_for_needy
1	0.02	support_for_other_democracies
1	0.02	support_of_allies
1	0.02	support_of_laws
1	0.02	supporting_democracy_and_human_rights_around_the_world
1	0.02	supporting_illegal_immigration
1	0.02	supportive_of_others
1	0.02	suppress_lower_classes
1	0.02	suppression_of_rights
1	0.02	supremacy_of_law
1	0.02	surveilance
1	0.02	surveilance_of_the_people
1	0.02	survival_of_fittest
1	0.02	sustain_economy
1	0.02	sustainability
1	0.02	sustainable_energy
1	0.02	synthesis_of_ideas
1	0.02	systematic_racisim
1	0.02	systemic_suffering
1	0.02	take_away_arms
1	0.02	take_away_freedoms
1	0.02	talent
1	0.02	taxation_is_key
1	0.02	taxation_to_support_military
1	0.02	taxing
1	0.02	technological_advancements
1	0.02	technological_progress
1	0.02	technology
1	0.02	tenacity
1	0.02	term_limited_government
1	0.02	terrible_and_sad
1	0.02	terrorism
1	0.02	that_all_people_are_created_equal
1	0.02	the_1%
1	0.02	the_ability_to_critize__the_government
1	0.02	the_ability_to_own_a_gun
1	0.02	the_ability_to_protest
1	0.02	the_ends_justifies_the_means
1	0.02	the_president_has_no_power
1	0.02	the_president_rules_with_executive_order_regardless_of_the_government_structure
1	0.02	the_press_rules
1	0.02	the_pursuit_of_happiness
1	0.02	the_rich_should_be_rewarded_more
1	0.02	the_right_for_economic_liberty
1	0.02	the_right_for_equality
1	0.02	the_right_of_speech
1	0.02	the_right_to_build_a_future
1	0.02	the_right_to_education
1	0.02	the_right_to_individualism
1	0.02	the_right_to_protect_ones_land
1	0.02	the_right_to_protest_the_government
1	0.02	the_right_to_self_govern
1	0.02	the_right_to_unify
1	0.02	the_u.s._stands_for_equality
1	0.02	the_u.s._stands_for_individualism
1	0.02	the_u.s._stands_for_liberty
1	0.02	the_u.s._stands_for_unity
1	0.02	the_u.s_stands_for_diversity
1	0.02	the_will_of_the_people_will_be_heard_and_obeyed
1	0.02	theft
1	0.02	theocracy
1	0.02	there_should_be_separation_between_church_and_state
1	0.02	there_will_be_no_monarchy
1	0.02	they_don’t_value_us
1	0.02	they_side_with_other_countries
1	0.02	they_stand_for_others
1	0.02	they_take_things_too_easy
1	0.02	thorough
1	0.02	those_with_enough_$$_can_buy_justice
1	0.02	those_with_money_get_away_with_crimes_that_others_are_put_to_jail_for
1	0.02	three_government_branches
1	0.02	time_management
1	0.02	to_be_able_to_freely_speak
1	0.02	to_be_able_to_practice_ones_religion_freely
1	0.02	to_be_able_to_protect_self_and_family
1	0.02	to_be_secure/safe
1	0.02	to_be_the_best_in_the_world
1	0.02	to_dictate_what_others_do
1	0.02	to_honor_our_country
1	0.02	to_love_freely
1	0.02	to_seem_free
1	0.02	together
1	0.02	togetherness
1	0.02	tolerance_of_differences
1	0.02	too_much_debt
1	0.02	too_much_liberalism
1	0.02	too_much_wrong
1	0.02	total_freedom
1	0.02	trade
1	0.02	trading
1	0.02	traditional
1	0.02	traditional_families
1	0.02	transferring_wealth_from_the_poor_to_the_rich
1	0.02	transparency
1	0.02	transparent
1	0.02	trasnperency
1	0.02	treated_equally
1	0.02	treating_everyone_equal
1	0.02	trial_by_peers
1	0.02	trials
1	0.02	true
1	0.02	trust_in_government
1	0.02	trustworthiness
1	0.02	truthful
1	0.02	truty
1	0.02	two_party_system
1	0.02	u.s._constitution
1	0.02	u.s_stands_for_advancement_in_technologies_and_research
1	0.02	u.s_stands_for_democracy_in_practice
1	0.02	ukraine
1	0.02	understanding_of_others
1	0.02	unequal_justice
1	0.02	unfair
1	0.02	unfair_elections
1	0.02	unfair_freedoms
1	0.02	unhappy
1	0.02	uniqueness
1	0.02	united
1	0.02	unity_and_diversity
1	0.02	universal_sufferage
1	0.02	unjust
1	0.02	unregulated_markets
1	0.02	unsafe
1	0.02	upholding_rule_of_law_for_all
1	0.02	upholding_the_constitution.
1	0.02	us_is_willing_to_abdicate_soverignty.
1	0.02	valor_and_demeanor
1	0.02	value_humanity
1	0.02	value_of_democracy
1	0.02	value_of_equality
1	0.02	value_of_individualism
1	0.02	value_of_privacy
1	0.02	value_of_work
1	0.02	value_yourself
1	0.02	values_based_on_the_bible
1	0.02	values_diversity,_welcoming
1	0.02	victory
1	0.02	vilifying_nonconformity.
1	0.02	violation_of_people’s_rights
1	0.02	virtue
1	0.02	voice_and_participation
1	0.02	voice_to_be_heard
1	0.02	voluntarism
1	0.02	vote_on_party_lines
1	0.02	vote_your_choice
1	0.02	voting_privileges
1	0.02	voting_representation
1	0.02	voting_right
1	0.02	voting_rights_are_sacred_and_should_be_not_be_unduly_impeded
1	0.02	voting_rights_for_citizens_only
1	0.02	war_mongering
1	0.02	wars
1	0.02	waste
1	0.02	watching_sports_for_fun
1	0.02	way_of_life
1	0.02	we_also_believe_in_equality
1	0.02	we_also_believe_in_inclusion
1	0.02	we_are_free!
1	0.02	we_are_still_the_greatest_constitutional_republic
1	0.02	we_the_people
1	0.02	we_value_democracy
1	0.02	wealth_extraction
1	0.02	wealth_gain
1	0.02	wealth_privilege
1	0.02	wealthy_win
1	0.02	weaponization_of_justice
1	0.02	weaponize_the_judicial_system_against_dissenters.
1	0.02	weapons_rights
1	0.02	weird_thought_experiments.
1	0.02	welcoming
1	0.02	welcoming_for_anyone_who_needs_refuge
1	0.02	welcoming_immigrants
1	0.02	welcoming_to_all
1	0.02	well_being
1	0.02	western_societal_values
1	0.02	what_is_right
1	0.02	when_things_are_tough,_the_us_will_widthraw
1	0.02	white_dominance
1	0.02	white_power
1	0.02	white_supremacy
1	0.02	wholesome_food
1	0.02	widest_job_opportunities
1	0.02	wilds_influential_power
1	0.02	willing_to_fail
1	0.02	win
1	0.02	winning_at_all_cost
1	0.02	wokeness
1	0.02	women_rights
1	0.02	work_anywhere
1	0.02	work_hard
1	0.02	work_wherever_you_want
1	0.02	workers
1	0.02	working_for_a_great_company
1	0.02	working_people
1	0.02	world_bully
1	0.02	world_disrupter
1	0.02	world_freedom
1	0.02	world_order
1	0.02	world_police
1	0.02	world_security
1	0.02	world_trade
1	0.02	worldwide_control
1	0.02	worship
1	0.02	worship_god
1	0.02	worship_of_black_people
1	0.02	zenophobia
1	0.02	zionism

Cosine similarity: GloVe

After getting the values’ semantic meaning in 100-dimension vectors, I took the weighted mean of each perspective per participant and took the cosine similarity between the two persepctives’ vectors. This is the distribution:

df_bsc_elg %>%
  ggplot() +
  geom_density(aes(x = simi), fill = "lightblue") +
  geom_vline(xintercept = mean(df_bsc_elg$simi,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"))

Cosine similarity: OpenAI

I also got word embeddings through the OpenAI API. There, I used the [text-embedding-3-large] model that gives 3072 dimensions per term. It’s much more exhaustive and much more precise. Then, I did the same procesure for the weighted cosine similarities.

df_bsc_elg %>%
  ggplot() +
  geom_density(aes(x = simi_openai), fill = "lightblue") +
  geom_vline(xintercept = mean(df_bsc_elg$simi_openai,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"))

Anti-Establishment

1. The US’s economy is rigged to advantage the rich and powerful
   
2. Traditional politicians and parties don’t care about people like me
   
3. Experts in this country don’t understand the lives of people like me
   
4. Most of the time we can trust people in the government to do what is right [R]
   alpha = 0.75

df_bsc_elg %>% 
  ggplot(aes(x = antiest)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$antiest,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Trust in democratic institutions

Please indicate how much you trust or distrust the following institutions (1 = Strongly Distrust to 7 = Strongly Trust)

1. The US Congress / Legislative Branch
2. The US Government / Executive Branch
3. The US Courts / Judicial Branch

alpha = 0.84

df_bsc_elg %>% 
  ggplot(aes(x = trust_deminst)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$trust_deminst,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Trust in national mainstream institutions

Please indicate how much you trust or distrust the following institutions (1 = Strongly Distrust to 7 = Strongly Trust)

1. Mainstream media in the US (e.g., CNN, FOX News, MSNBC, New York Times, Wall-Street Journal, USA Today)
2. The education system in the US
3. Law enforcement / police in the US
4. The US Military
5. Financial institutions in the US (e.g., Wall Street, The Fed, The Big Banks)
6. The medical system in the US

alpha = 0.81

df_bsc_elg %>% 
  ggplot(aes(x = trust_natinst)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$trust_natinst,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Trust in science

Please indicate how much you agree or disagree with the following statements (1 = Strongly Disagree to 7 = Strongly Agree)

1. I generally trust the recommendations of scientists
2. Scientific institutions generate objective knowledge
3. I look to the social sciences for answers to social problems

alpha = 0.86

df_bsc_elg %>% 
  ggplot(aes(x = trust_science)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$trust_science,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Support for radical change

To what extent do you agree with the following statement?

The way this country works needs to be radically changed

df_bsc_elg %>% 
  ggplot(aes(x = change)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$change,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

SDO

1. An ideal society requires some groups to be on top and others to be on the bottom
   
2. Some groups of people are simply inferior to other groups
   
3. No one group should dominate in society [R]
   
4. Groups at the bottom are just as deserving as groups at the top [R]
   
5. Group equality should not be our primary goal
   
6. It is unjust to try to make groups equal
   
7. We should do what we can to equalize conditions for different groups [R]
   
8. We should work to give all groups an equal chance to succeed [R]
   
   alpha = 0.9

df_bsc_elg %>% 
  ggplot(aes(x = SDO)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$SDO,na.rm = T),
             color = "grey15",
             size = 1,
             linetype = "dashed") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

TIPI

I see myself as… (1 = Strongly Disagree to 7 = Strongly Agree)

1. Extraverted, enthusiastic
   
2. Critical, quarrelsome [R]
   
3. Dependable, self-disciplined
   
4. Anxious, easily upset
   
5. Open to new experiences, complex
   
6. Reserved, quiet [R]
   
7. Sympathetic, warm
   
8. Disorganized, careless [R]
   
9. Calm, emotionally stable [R]
   
10. Conventional, uncreative [R]
    
    Extraversion: Mean score of items 1 and 6
    Agreeableness: Mean score of items 2 and 7
    Conscientiousness: Mean score of items 3 and 8
    Neuroticism: Mean score of items 4 and 9
    Openness: Mean score of items 5 and 10

means <- df_bsc_elg %>%
  dplyr::select(PID,TIPI_extra:TIPI_open) %>% 
  pivot_longer(-PID,
               names_to = "trait",
               values_to = "score") %>% 
  filter(!is.na(score)) %>% 
  group_by(trait) %>% 
  summarise(score = mean(score)) %>% 
  ungroup()

df_bsc_elg %>%
  dplyr::select(PID,TIPI_extra:TIPI_open) %>% 
  pivot_longer(-PID,
               names_to = "trait",
               values_to = "score") %>% 
  filter(!is.na(score)) %>%  
  ggplot() +
  geom_density(aes(x = score), fill = "lightblue") +
  scale_x_continuous(limits = c(1,7),
                     breaks = seq(1,7,1)) +
  geom_vline(data = means,mapping = aes(xintercept = score),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold")) +
  facet_wrap(~trait,nrow = 2)

Voting intentions

What is the likelihood that you will vote in the 2024 Presidential Elections?

df_bsc_elg %>% 
  ggplot(aes(x = vote_likely)) +
  geom_histogram(fill = "lightblue",
                 color = "black",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(0,6)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$vote_likely,na.rm = T),
             color = "grey15",
             size = 1,
             linetype = "dashed") +
  theme(panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.background = element_blank(),
        axis.ticks = element_blank(),
        axis.line = element_line(color = "grey66"),
        axis.text.y = element_text(color = "black"),
        axis.text.x = element_text(color = "black",
                                   face = "bold"),
        axis.title.x = element_text(color = "black",
                                   face = "bold"))

Analysis

Correlation matrix

Similarity score, likelihood to vote in the 2024 Presidential election, support for radical change, anti-establishment sentiment, trust in democratic institutions, trust in mainstream societal institutions, trust in science, conservatism, SDO, TIPI extraversion, TIPI agreeableness, TIPI Conscientiousness, TIPI neuroticism, TIPI openness.

df_bsc_elg %>% 
  dplyr::select(simi,simi_openai,vote_likely,change,antiest:trust_science,SDO,ideo_con,TIPI_extra:TIPI_open) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

Outcome Variable: Likelihood to vote in the 2024 Presidential election

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(vote_likely_z ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-31)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.03	[-0.03, 0.08]	0.95	1120	.343
Simi z	0.01	[-0.05, 0.07]	0.34	1120	.737
Ideo con z	0.12	[0.06, 0.18]	4.08	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(vote_likely_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-32)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.03	[-0.03, 0.08]	0.98	1118	.327
Simi z	0.00	[-0.05, 0.06]	0.14	1118	.892
Ideo con z	0.18	[0.11, 0.24]	5.27	1118	< .001
SDO z	-0.11	[-0.18, -0.05]	-3.29	1118	.001
TIPI agree z	0.12	[0.06, 0.18]	4.01	1118	< .001

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(vote_likely_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-33)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.90	[-2.68, 0.88]	-0.99	1004	.322
Simi z	0.04	[-0.02, 0.09]	1.25	1004	.212
Ideo con z	0.19	[0.13, 0.26]	5.75	1004	< .001
SDO z	-0.13	[-0.19, -0.06]	-3.82	1004	< .001
TIPI agree z	0.05	[-0.01, 0.11]	1.66	1004	.097
Man	-0.03	[-0.14, 0.09]	-0.46	1004	.649
RaceAsian	1.07	[-0.72, 2.87]	1.18	1004	.240
RaceBlack or African American	0.79	[-0.99, 2.58]	0.87	1004	.382
RaceMiddle Eastern or North African	-0.05	[-2.23, 2.13]	-0.04	1004	.967
Racemultiracial	0.94	[-0.87, 2.74]	1.02	1004	.308
RaceNative Hawaiian or Other Pacific Islander	1.92	[-0.59, 4.42]	1.50	1004	.135
RaceOther please specify	0.91	[-1.00, 2.83]	0.94	1004	.350
RaceWhite	0.90	[-0.88, 2.68]	0.99	1004	.321
Hispanic	0.19	[-0.03, 0.42]	1.70	1004	.090
Income num z	0.06	[0.00, 0.12]	2.11	1004	.035
Edu num z	0.08	[0.02, 0.14]	2.65	1004	.008
Age z	0.33	[0.26, 0.39]	9.81	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(vote_likely_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-34)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.86	[-2.65, 0.94]	-0.94	966	.348
Simi z	0.04	[-0.02, 0.10]	1.31	966	.191
Ideo con z	0.20	[0.13, 0.26]	5.67	966	< .001
SDO z	-0.13	[-0.20, -0.06]	-3.78	966	< .001
TIPI agree z	0.05	[-0.01, 0.11]	1.63	966	.103
Man	-0.03	[-0.15, 0.09]	-0.46	966	.648
RaceAsian	1.03	[-0.78, 2.84]	1.12	966	.264
RaceBlack or African American	0.79	[-1.01, 2.58]	0.86	966	.389
RaceMiddle Eastern or North African	-0.03	[-2.22, 2.17]	-0.02	966	.982
Racemultiracial	0.90	[-0.91, 2.72]	0.98	966	.330
RaceNative Hawaiian or Other Pacific Islander	1.86	[-0.67, 4.39]	1.44	966	.149
RaceOther please specify	0.86	[-1.08, 2.80]	0.87	966	.383
RaceWhite	0.84	[-0.95, 2.63]	0.92	966	.360
Hispanic	0.22	[-0.01, 0.45]	1.87	966	.061
Income num z	0.06	[0.00, 0.12]	1.92	966	.056
Edu num z	0.08	[0.02, 0.14]	2.49	966	.013
Age z	0.34	[0.27, 0.40]	9.79	966	< .001
County medianincome z	0.04	[-0.02, 0.10]	1.28	966	.201
County gini z	-0.05	[-0.12, 0.02]	-1.47	966	.142
County density z	0.00	[-0.07, 0.07]	0.01	966	.991

Outcome Variable: Support for radical change

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(change_z ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-35)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.04]	-0.74	1120	.460
Simi z	-0.09	[-0.15, -0.03]	-3.02	1120	.003
Ideo con z	-0.20	[-0.25, -0.14]	-6.68	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(change_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-36)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.04]	-0.70	1118	.483
Simi z	-0.09	[-0.15, -0.03]	-3.05	1118	.002
Ideo con z	-0.16	[-0.23, -0.09]	-4.62	1118	< .001
SDO z	-0.07	[-0.14, -0.01]	-2.13	1118	.033
TIPI agree z	-0.03	[-0.09, 0.03]	-0.95	1118	.342

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(change_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-37)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.62	[-2.44, 1.20]	-0.67	1004	.502
Simi z	-0.10	[-0.15, -0.04]	-3.31	1004	< .001
Ideo con z	-0.14	[-0.20, -0.07]	-3.99	1004	< .001
SDO z	-0.04	[-0.10, 0.03]	-1.03	1004	.301
TIPI agree z	-0.02	[-0.08, 0.04]	-0.50	1004	.621
Man	-0.30	[-0.41, -0.18]	-4.92	1004	< .001
RaceAsian	0.71	[-1.13, 2.54]	0.76	1004	.449
RaceBlack or African American	0.91	[-0.91, 2.74]	0.98	1004	.326
RaceMiddle Eastern or North African	2.03	[-0.20, 4.26]	1.78	1004	.075
Racemultiracial	0.81	[-1.03, 2.65]	0.86	1004	.389
RaceNative Hawaiian or Other Pacific Islander	0.71	[-1.86, 3.28]	0.54	1004	.588
RaceOther please specify	0.96	[-1.00, 2.92]	0.96	1004	.337
RaceWhite	0.71	[-1.11, 2.53]	0.77	1004	.444
Hispanic	0.11	[-0.12, 0.34]	0.94	1004	.350
Income num z	-0.11	[-0.17, -0.05]	-3.79	1004	< .001
Edu num z	-0.08	[-0.14, -0.03]	-2.81	1004	.005
Age z	-0.25	[-0.31, -0.18]	-7.22	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(change_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-38)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.65	[-2.47, 1.17]	-0.70	966	.484
Simi z	-0.09	[-0.15, -0.03]	-3.09	966	.002
Ideo con z	-0.14	[-0.21, -0.07]	-3.99	966	< .001
SDO z	-0.04	[-0.11, 0.03]	-1.20	966	.230
TIPI agree z	-0.01	[-0.08, 0.05]	-0.47	966	.639
Man	-0.30	[-0.42, -0.18]	-4.87	966	< .001
RaceAsian	0.75	[-1.09, 2.58]	0.80	966	.425
RaceBlack or African American	0.96	[-0.86, 2.79]	1.03	966	.301
RaceMiddle Eastern or North African	2.02	[-0.21, 4.25]	1.78	966	.076
Racemultiracial	0.83	[-1.01, 2.68]	0.89	966	.376
RaceNative Hawaiian or Other Pacific Islander	0.75	[-1.82, 3.32]	0.58	966	.565
RaceOther please specify	1.01	[-0.96, 2.98]	1.00	966	.317
RaceWhite	0.73	[-1.09, 2.55]	0.79	966	.429
Hispanic	0.16	[-0.07, 0.39]	1.35	966	.177
Income num z	-0.10	[-0.16, -0.04]	-3.36	966	< .001
Edu num z	-0.08	[-0.14, -0.02]	-2.60	966	.009
Age z	-0.24	[-0.31, -0.17]	-6.86	966	< .001
County medianincome z	-0.01	[-0.07, 0.05]	-0.26	966	.793
County gini z	-0.05	[-0.12, 0.03]	-1.23	966	.217
County density z	0.03	[-0.04, 0.10]	0.88	966	.380

Outcome Variable: Anti-establishment sentiment

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(antiest_z ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-39)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.06, 0.06]	-0.02	1120	.988
Simi z	-0.11	[-0.17, -0.05]	-3.73	1120	< .001
Ideo con z	-0.11	[-0.17, -0.06]	-3.85	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(antiest_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-40)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.06, 0.06]	-0.01	1118	.989
Simi z	-0.11	[-0.16, -0.05]	-3.62	1118	< .001
Ideo con z	-0.14	[-0.21, -0.07]	-4.09	1118	< .001
SDO z	0.05	[-0.02, 0.12]	1.47	1118	.141
TIPI agree z	-0.09	[-0.15, -0.03]	-3.07	1118	.002

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(antiest_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-41)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.20	[-2.10, 1.69]	-0.21	1004	.833
Simi z	-0.11	[-0.17, -0.05]	-3.60	1004	< .001
Ideo con z	-0.13	[-0.20, -0.06]	-3.66	1004	< .001
SDO z	0.07	[0.00, 0.14]	2.04	1004	.042
TIPI agree z	-0.08	[-0.14, -0.02]	-2.51	1004	.012
Man	0.01	[-0.11, 0.14]	0.20	1004	.845
RaceAsian	0.21	[-1.70, 2.12]	0.22	1004	.827
RaceBlack or African American	0.07	[-1.83, 1.97]	0.07	1004	.944
RaceMiddle Eastern or North African	0.20	[-2.12, 2.52]	0.17	1004	.866
Racemultiracial	0.04	[-1.88, 1.96]	0.04	1004	.967
RaceNative Hawaiian or Other Pacific Islander	0.18	[-2.50, 2.85]	0.13	1004	.897
RaceOther please specify	0.01	[-2.03, 2.05]	0.01	1004	.992
RaceWhite	0.20	[-1.69, 2.09]	0.21	1004	.836
Hispanic	0.05	[-0.19, 0.29]	0.41	1004	.684
Income num z	-0.14	[-0.21, -0.08]	-4.64	1004	< .001
Edu num z	-0.11	[-0.17, -0.05]	-3.60	1004	< .001
Age z	-0.11	[-0.18, -0.04]	-3.21	1004	.001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(antiest_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-42)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.29	[-2.18, 1.61]	-0.30	966	.768
Simi z	-0.10	[-0.17, -0.04]	-3.34	966	< .001
Ideo con z	-0.14	[-0.21, -0.07]	-3.81	966	< .001
SDO z	0.08	[0.01, 0.15]	2.20	966	.028
TIPI agree z	-0.08	[-0.14, -0.02]	-2.46	966	.014
Man	0.01	[-0.12, 0.13]	0.12	966	.901
RaceAsian	0.31	[-1.61, 2.22]	0.31	966	.754
RaceBlack or African American	0.18	[-1.72, 2.08]	0.19	966	.852
RaceMiddle Eastern or North African	0.33	[-2.00, 2.65]	0.28	966	.783
Racemultiracial	0.12	[-1.80, 2.04]	0.12	966	.901
RaceNative Hawaiian or Other Pacific Islander	0.28	[-2.40, 2.95]	0.20	966	.839
RaceOther please specify	0.18	[-1.88, 2.23]	0.17	966	.866
RaceWhite	0.28	[-1.61, 2.18]	0.29	966	.769
Hispanic	0.08	[-0.16, 0.32]	0.64	966	.525
Income num z	-0.14	[-0.20, -0.08]	-4.35	966	< .001
Edu num z	-0.10	[-0.16, -0.04]	-3.14	966	.002
Age z	-0.12	[-0.19, -0.05]	-3.26	966	.001
County medianincome z	-0.02	[-0.09, 0.04]	-0.68	966	.496
County gini z	-0.05	[-0.13, 0.02]	-1.38	966	.167
County density z	-0.01	[-0.08, 0.06]	-0.21	966	.833

Outcome Variable: Trust in democratic institutions

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(trust_deminst_z ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-43)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.05, 0.06]	0.11	1120	.912
Simi z	0.13	[0.07, 0.19]	4.47	1120	< .001
Ideo con z	0.07	[0.01, 0.13]	2.29	1120	.022

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(trust_deminst_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-44)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.05, 0.06]	0.12	1118	.905
Simi z	0.13	[0.07, 0.18]	4.33	1118	< .001
Ideo con z	0.11	[0.05, 0.18]	3.33	1118	< .001
SDO z	-0.09	[-0.16, -0.02]	-2.57	1118	.010
TIPI agree z	0.12	[0.06, 0.18]	3.90	1118	< .001

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(trust_deminst_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-45)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.98	[-0.94, 2.89]	1.00	1004	.317
Simi z	0.12	[0.06, 0.18]	3.77	1004	< .001
Ideo con z	0.11	[0.04, 0.18]	3.13	1004	.002
SDO z	-0.11	[-0.18, -0.04]	-2.95	1004	.003
TIPI agree z	0.11	[0.05, 0.18]	3.49	1004	< .001
Man	-0.02	[-0.14, 0.11]	-0.29	1004	.775
RaceAsian	-0.86	[-2.78, 1.07]	-0.87	1004	.384
RaceBlack or African American	-0.76	[-2.67, 1.16]	-0.78	1004	.437
RaceMiddle Eastern or North African	-1.65	[-3.99, 0.70]	-1.38	1004	.168
Racemultiracial	-0.99	[-2.93, 0.95]	-1.00	1004	.316
RaceNative Hawaiian or Other Pacific Islander	-0.69	[-3.39, 2.00]	-0.50	1004	.614
RaceOther please specify	-1.22	[-3.28, 0.84]	-1.16	1004	.245
RaceWhite	-0.96	[-2.87, 0.95]	-0.99	1004	.324
Hispanic	-0.11	[-0.35, 0.13]	-0.88	1004	.379
Income num z	0.06	[0.00, 0.13]	2.04	1004	.041
Edu num z	0.12	[0.05, 0.18]	3.63	1004	< .001
Age z	0.01	[-0.06, 0.08]	0.33	1004	.744

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_deminst_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-46)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	1.11	[-0.79, 3.02]	1.15	966	.252
Simi z	0.11	[0.05, 0.18]	3.66	966	< .001
Ideo con z	0.13	[0.05, 0.20]	3.40	966	< .001
SDO z	-0.11	[-0.19, -0.04]	-3.13	966	.002
TIPI agree z	0.12	[0.05, 0.18]	3.55	966	< .001
Man	-0.02	[-0.15, 0.10]	-0.34	966	.736
RaceAsian	-1.00	[-2.92, 0.92]	-1.02	966	.308
RaceBlack or African American	-0.91	[-2.82, 1.00]	-0.94	966	.348
RaceMiddle Eastern or North African	-1.86	[-4.19, 0.48]	-1.56	966	.119
Racemultiracial	-1.12	[-3.05, 0.81]	-1.14	966	.254
RaceNative Hawaiian or Other Pacific Islander	-0.85	[-3.53, 1.84]	-0.62	966	.537
RaceOther please specify	-1.48	[-3.54, 0.58]	-1.41	966	.159
RaceWhite	-1.09	[-3.00, 0.81]	-1.13	966	.260
Hispanic	-0.11	[-0.35, 0.13]	-0.89	966	.373
Income num z	0.05	[-0.01, 0.12]	1.70	966	.090
Edu num z	0.09	[0.03, 0.16]	2.93	966	.003
Age z	0.03	[-0.05, 0.10]	0.71	966	.478
County medianincome z	0.04	[-0.03, 0.11]	1.19	966	.234
County gini z	0.04	[-0.04, 0.11]	0.96	966	.336
County density z	0.05	[-0.03, 0.12]	1.23	966	.220

Outcome Variable: Trust in national mainstream institutions

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(trust_natinst_z ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-47)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.07]	0.34	1120	.737
Simi z	0.12	[0.06, 0.17]	3.96	1120	< .001
Ideo con z	0.09	[0.03, 0.15]	3.08	1120	.002

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(trust_natinst_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-48)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.07]	0.33	1118	.740
Simi z	0.11	[0.05, 0.17]	3.79	1118	< .001
Ideo con z	0.14	[0.07, 0.20]	4.03	1118	< .001
SDO z	-0.09	[-0.15, -0.02]	-2.51	1118	.012
TIPI agree z	0.19	[0.13, 0.25]	6.40	1118	< .001

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(trust_natinst_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-49)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.27	[-1.59, 2.13]	0.28	1004	.777
Simi z	0.11	[0.05, 0.17]	3.73	1004	< .001
Ideo con z	0.12	[0.05, 0.19]	3.49	1004	< .001
SDO z	-0.10	[-0.17, -0.03]	-2.80	1004	.005
TIPI agree z	0.16	[0.10, 0.22]	5.05	1004	< .001
Man	0.02	[-0.10, 0.14]	0.37	1004	.709
RaceAsian	-0.13	[-2.01, 1.74]	-0.14	1004	.889
RaceBlack or African American	-0.09	[-1.96, 1.78]	-0.10	1004	.924
RaceMiddle Eastern or North African	-0.79	[-3.07, 1.49]	-0.68	1004	.497
Racemultiracial	-0.35	[-2.24, 1.54]	-0.36	1004	.716
RaceNative Hawaiian or Other Pacific Islander	0.51	[-2.12, 3.14]	0.38	1004	.703
RaceOther please specify	-0.72	[-2.73, 1.28]	-0.71	1004	.479
RaceWhite	-0.28	[-2.14, 1.59]	-0.29	1004	.771
Hispanic	-0.08	[-0.31, 0.15]	-0.67	1004	.502
Income num z	0.11	[0.05, 0.17]	3.57	1004	< .001
Edu num z	0.10	[0.04, 0.16]	3.12	1004	.002
Age z	0.17	[0.10, 0.24]	4.88	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_natinst_z ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-50)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.39	[-1.46, 2.24]	0.41	966	.681
Simi z	0.11	[0.05, 0.17]	3.76	966	< .001
Ideo con z	0.13	[0.06, 0.20]	3.63	966	< .001
SDO z	-0.11	[-0.18, -0.04]	-3.06	966	.002
TIPI agree z	0.16	[0.10, 0.22]	5.08	966	< .001
Man	0.03	[-0.09, 0.15]	0.51	966	.608
RaceAsian	-0.25	[-2.11, 1.62]	-0.26	966	.794
RaceBlack or African American	-0.22	[-2.08, 1.63]	-0.24	966	.812
RaceMiddle Eastern or North African	-0.98	[-3.24, 1.29]	-0.85	966	.397
Racemultiracial	-0.47	[-2.34, 1.40]	-0.49	966	.623
RaceNative Hawaiian or Other Pacific Islander	0.37	[-2.24, 2.97]	0.28	966	.783
RaceOther please specify	-0.98	[-2.98, 1.02]	-0.96	966	.338
RaceWhite	-0.40	[-2.24, 1.45]	-0.42	966	.674
Hispanic	-0.11	[-0.34, 0.13]	-0.88	966	.380
Income num z	0.10	[0.04, 0.16]	3.10	966	.002
Edu num z	0.08	[0.02, 0.14]	2.50	966	.013
Age z	0.18	[0.11, 0.25]	5.18	966	< .001
County medianincome z	0.05	[-0.02, 0.11]	1.46	966	.146
County gini z	-0.01	[-0.09, 0.06]	-0.38	966	.702
County density z	0.08	[0.00, 0.15]	2.09	966	.037

Exploratory analysis

Same models from the analysis plan, but with the following outcome variables: (1) Binary voting behavior; (2) trust in science.

Outcome Variable: Binary voting behavior

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(vote_tomorrow ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-51)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.89	[0.87, 0.91]	95.09	1120	< .001
Simi z	0.01	[-0.01, 0.03]	1.01	1120	.314
Ideo con z	0.03	[0.01, 0.05]	3.44	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(vote_tomorrow ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-52)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.89	[0.87, 0.91]	95.50	1118	< .001
Simi z	0.01	[-0.01, 0.03]	0.90	1118	.371
Ideo con z	0.04	[0.02, 0.06]	3.41	1118	< .001
SDO z	-0.01	[-0.03, 0.01]	-0.85	1118	.394
TIPI agree z	0.03	[0.01, 0.05]	3.10	1118	.002

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(vote_tomorrow ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-53)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	1.00	[0.40, 1.61]	3.25	1004	.001
Simi z	0.02	[0.00, 0.04]	1.64	1004	.100
Ideo con z	0.05	[0.02, 0.07]	3.96	1004	< .001
SDO z	-0.01	[-0.04, 0.01]	-1.22	1004	.223
TIPI agree z	0.02	[0.00, 0.04]	1.86	1004	.062
Man	0.00	[-0.04, 0.04]	-0.09	1004	.929
RaceAsian	-0.04	[-0.65, 0.57]	-0.14	1004	.886
RaceBlack or African American	-0.15	[-0.75, 0.46]	-0.47	1004	.639
RaceMiddle Eastern or North African	-0.46	[-1.20, 0.29]	-1.21	1004	.228
Racemultiracial	-0.13	[-0.74, 0.48]	-0.42	1004	.677
RaceNative Hawaiian or Other Pacific Islander	0.12	[-0.74, 0.97]	0.27	1004	.786
RaceOther please specify	-0.12	[-0.77, 0.53]	-0.36	1004	.722
RaceWhite	-0.12	[-0.73, 0.48]	-0.40	1004	.687
Hispanic	0.03	[-0.05, 0.10]	0.66	1004	.508
Income num z	0.01	[-0.01, 0.03]	0.94	1004	.347
Edu num z	0.04	[0.02, 0.06]	3.58	1004	< .001
Age z	0.06	[0.04, 0.08]	5.44	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(vote_tomorrow ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-54)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	1.00	[0.39, 1.61]	3.23	966	.001
Simi z	0.02	[0.00, 0.04]	1.76	966	.078
Ideo con z	0.05	[0.02, 0.07]	3.92	966	< .001
SDO z	-0.02	[-0.04, 0.01]	-1.39	966	.165
TIPI agree z	0.02	[0.00, 0.04]	1.74	966	.081
Man	0.00	[-0.04, 0.04]	-0.15	966	.879
RaceAsian	-0.04	[-0.66, 0.57]	-0.13	966	.894
RaceBlack or African American	-0.14	[-0.75, 0.47]	-0.45	966	.649
RaceMiddle Eastern or North African	-0.46	[-1.21, 0.29]	-1.21	966	.226
Racemultiracial	-0.13	[-0.75, 0.49]	-0.41	966	.678
RaceNative Hawaiian or Other Pacific Islander	0.12	[-0.74, 0.98]	0.28	966	.783
RaceOther please specify	-0.12	[-0.78, 0.54]	-0.36	966	.721
RaceWhite	-0.13	[-0.74, 0.48]	-0.42	966	.674
Hispanic	0.03	[-0.04, 0.11]	0.85	966	.398
Income num z	0.01	[-0.01, 0.03]	1.07	966	.286
Edu num z	0.04	[0.02, 0.06]	3.48	966	< .001
Age z	0.07	[0.04, 0.09]	5.70	966	< .001
County medianincome z	0.00	[-0.02, 0.02]	0.30	966	.762
County gini z	-0.02	[-0.05, 0.00]	-1.83	966	.068
County density z	0.01	[-0.01, 0.04]	1.22	966	.221

Outcome Variable: Trust in science

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(trust_science ~ simi_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-55)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.85	[4.78, 4.91]	139.57	1120	< .001
Simi z	0.03	[-0.04, 0.10]	0.85	1120	.394
Ideo con z	-0.55	[-0.62, -0.48]	-15.87	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(trust_science ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-56)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.85	[4.79, 4.92]	147.67	1118	< .001
Simi z	0.02	[-0.05, 0.08]	0.59	1118	.558
Ideo con z	-0.33	[-0.41, -0.26]	-8.71	1118	< .001
SDO z	-0.42	[-0.50, -0.35]	-10.87	1118	< .001
TIPI agree z	0.04	[-0.02, 0.11]	1.29	1118	.197

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(trust_science ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-57)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.80	[2.67, 6.92]	4.44	1004	< .001
Simi z	0.01	[-0.06, 0.08]	0.24	1004	.807
Ideo con z	-0.32	[-0.40, -0.24]	-8.01	1004	< .001
SDO z	-0.43	[-0.51, -0.35]	-10.73	1004	< .001
TIPI agree z	0.10	[0.03, 0.17]	2.68	1004	.008
Man	0.09	[-0.05, 0.22]	1.24	1004	.215
RaceAsian	0.23	[-1.91, 2.37]	0.21	1004	.834
RaceBlack or African American	-0.23	[-2.36, 1.89]	-0.21	1004	.830
RaceMiddle Eastern or North African	0.78	[-1.82, 3.38]	0.59	1004	.555
Racemultiracial	-0.08	[-2.23, 2.07]	-0.07	1004	.944
RaceNative Hawaiian or Other Pacific Islander	1.39	[-1.60, 4.39]	0.91	1004	.362
RaceOther please specify	-1.06	[-3.35, 1.23]	-0.91	1004	.363
RaceWhite	0.06	[-2.06, 2.18]	0.06	1004	.954
Hispanic	-0.04	[-0.31, 0.23]	-0.30	1004	.761
Income num z	0.10	[0.03, 0.17]	2.80	1004	.005
Edu num z	0.17	[0.10, 0.24]	4.78	1004	< .001
Age z	-0.15	[-0.23, -0.07]	-3.77	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_science ~ simi_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-58)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	4.90	[2.79, 7.01]	4.56	966	< .001
Simi z	0.01	[-0.06, 0.08]	0.24	966	.810
Ideo con z	-0.33	[-0.41, -0.25]	-8.01	966	< .001
SDO z	-0.45	[-0.53, -0.37]	-11.05	966	< .001
TIPI agree z	0.09	[0.02, 0.16]	2.48	966	.013
Man	0.10	[-0.04, 0.24]	1.45	966	.149
RaceAsian	0.12	[-2.01, 2.25]	0.11	966	.912
RaceBlack or African American	-0.32	[-2.44, 1.79]	-0.30	966	.763
RaceMiddle Eastern or North African	0.59	[-1.99, 3.18]	0.45	966	.653
Racemultiracial	-0.19	[-2.33, 1.94]	-0.18	966	.860
RaceNative Hawaiian or Other Pacific Islander	1.24	[-1.73, 4.22]	0.82	966	.412
RaceOther please specify	-1.31	[-3.60, 0.97]	-1.13	966	.259
RaceWhite	-0.06	[-2.17, 2.05]	-0.06	966	.954
Hispanic	-0.06	[-0.33, 0.21]	-0.45	966	.655
Income num z	0.08	[0.01, 0.15]	2.21	966	.027
Edu num z	0.16	[0.09, 0.23]	4.38	966	< .001
Age z	-0.14	[-0.22, -0.06]	-3.38	966	< .001
County medianincome z	0.05	[-0.02, 0.12]	1.30	966	.194
County gini z	-0.01	[-0.10, 0.07]	-0.31	966	.760
County density z	0.07	[-0.01, 0.15]	1.75	966	.080

Outcome Variable: Voting for Trump in 2024

m1 <- lm(vote_2024_trump ~ simi_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-59)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.38	[0.35, 0.41]	26.99	1186	< .001
Simi z	0.00	[-0.03, 0.03]	-0.10	1186	.918

Outcome Variable: Voting for Biden in 2024

m1 <- lm(vote_2024_biden ~ simi_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-60)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.43	[0.40, 0.45]	29.75	1186	< .001
Simi z	0.02	[0.00, 0.05]	1.69	1186	.091

Outcome Variable: Voting for RFK Jr. in 2024

m1 <- lm(vote_2024_rfkj ~ simi_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-61)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.09	[0.07, 0.10]	10.56	1186	< .001
Simi z	-0.01	[-0.03, 0.01]	-1.24	1186	.217

Outcome Variable: Voting for “other” in 2024

m1 <- lm(vote_2024_other ~ simi_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-62)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.07	[0.06, 0.08]	9.44	1186	< .001
Simi z	0.00	[-0.02, 0.01]	-0.33	1186	.742

Similarity score from OpenAI

I’ll do the same models as the analysis plan.

Outcome Variable: Likelihood to vote in the 2024 Presidential election

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(vote_likely_z ~ simi_openai_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-63)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.03	[-0.03, 0.08]	0.95	1120	.344
Simi openai z	0.03	[-0.02, 0.09]	1.19	1120	.233
Ideo con z	0.12	[0.06, 0.17]	4.05	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(vote_likely_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-64)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.03	[-0.03, 0.08]	0.98	1118	.328
Simi openai z	0.02	[-0.04, 0.08]	0.66	1118	.507
Ideo con z	0.18	[0.11, 0.24]	5.23	1118	< .001
SDO z	-0.11	[-0.18, -0.04]	-3.26	1118	.001
TIPI agree z	0.12	[0.06, 0.18]	3.96	1118	< .001

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(vote_likely_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-65)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.91	[-2.69, 0.87]	-1.00	1004	.317
Simi openai z	0.04	[-0.02, 0.09]	1.25	1004	.213
Ideo con z	0.19	[0.13, 0.26]	5.68	1004	< .001
SDO z	-0.13	[-0.19, -0.06]	-3.77	1004	< .001
TIPI agree z	0.05	[-0.01, 0.11]	1.62	1004	.107
Man	-0.03	[-0.14, 0.09]	-0.47	1004	.640
RaceAsian	1.08	[-0.71, 2.87]	1.18	1004	.237
RaceBlack or African American	0.80	[-0.98, 2.58]	0.88	1004	.378
RaceMiddle Eastern or North African	-0.04	[-2.22, 2.14]	-0.03	1004	.973
Racemultiracial	0.94	[-0.86, 2.75]	1.03	1004	.304
RaceNative Hawaiian or Other Pacific Islander	1.90	[-0.61, 4.41]	1.49	1004	.137
RaceOther please specify	0.94	[-0.98, 2.85]	0.96	1004	.336
RaceWhite	0.91	[-0.87, 2.69]	1.00	1004	.315
Hispanic	0.20	[-0.03, 0.42]	1.72	1004	.087
Income num z	0.06	[0.00, 0.12]	2.05	1004	.040
Edu num z	0.08	[0.02, 0.14]	2.62	1004	.009
Age z	0.32	[0.26, 0.39]	9.80	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(vote_likely_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-66)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.87	[-2.66, 0.92]	-0.95	966	.342
Simi openai z	0.04	[-0.02, 0.09]	1.26	966	.208
Ideo con z	0.19	[0.13, 0.26]	5.60	966	< .001
SDO z	-0.13	[-0.20, -0.06]	-3.73	966	< .001
TIPI agree z	0.05	[-0.01, 0.11]	1.58	966	.114
Man	-0.03	[-0.15, 0.09]	-0.48	966	.634
RaceAsian	1.04	[-0.77, 2.85]	1.13	966	.259
RaceBlack or African American	0.80	[-1.00, 2.59]	0.87	966	.384
RaceMiddle Eastern or North African	-0.02	[-2.21, 2.18]	-0.01	966	.989
Racemultiracial	0.91	[-0.90, 2.73]	0.99	966	.324
RaceNative Hawaiian or Other Pacific Islander	1.85	[-0.68, 4.38]	1.44	966	.151
RaceOther please specify	0.89	[-1.05, 2.83]	0.90	966	.367
RaceWhite	0.85	[-0.94, 2.64]	0.93	966	.352
Hispanic	0.22	[-0.01, 0.45]	1.89	966	.059
Income num z	0.06	[0.00, 0.12]	1.87	966	.061
Edu num z	0.08	[0.02, 0.13]	2.47	966	.014
Age z	0.33	[0.27, 0.40]	9.77	966	< .001
County medianincome z	0.04	[-0.02, 0.10]	1.25	966	.211
County gini z	-0.05	[-0.12, 0.02]	-1.45	966	.147
County density z	0.00	[-0.07, 0.07]	0.00	966	.996

Outcome Variable: Support for radical change

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(change_z ~ simi_openai_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-67)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.04]	-0.75	1120	.456
Simi openai z	-0.16	[-0.21, -0.10]	-5.43	1120	< .001
Ideo con z	-0.19	[-0.25, -0.13]	-6.59	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(change_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-68)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.04]	-0.71	1118	.480
Simi openai z	-0.16	[-0.22, -0.10]	-5.53	1118	< .001
Ideo con z	-0.15	[-0.22, -0.08]	-4.41	1118	< .001
SDO z	-0.08	[-0.15, -0.01]	-2.38	1118	.017
TIPI agree z	-0.02	[-0.08, 0.04]	-0.69	1118	.493

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(change_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-69)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.61	[-2.41, 1.20]	-0.66	1004	.508
Simi openai z	-0.15	[-0.21, -0.10]	-5.26	1004	< .001
Ideo con z	-0.13	[-0.20, -0.06]	-3.79	1004	< .001
SDO z	-0.04	[-0.11, 0.02]	-1.30	1004	.195
TIPI agree z	-0.01	[-0.07, 0.05]	-0.26	1004	.797
Man	-0.30	[-0.41, -0.18]	-4.94	1004	< .001
RaceAsian	0.71	[-1.11, 2.53]	0.77	1004	.443
RaceBlack or African American	0.91	[-0.90, 2.72]	0.99	1004	.323
RaceMiddle Eastern or North African	2.01	[-0.21, 4.22]	1.78	1004	.075
Racemultiracial	0.79	[-1.04, 2.61]	0.84	1004	.399
RaceNative Hawaiian or Other Pacific Islander	0.73	[-1.82, 3.27]	0.56	1004	.576
RaceOther please specify	0.84	[-1.10, 2.79]	0.85	1004	.394
RaceWhite	0.69	[-1.11, 2.50]	0.76	1004	.450
Hispanic	0.09	[-0.14, 0.32]	0.77	1004	.440
Income num z	-0.11	[-0.16, -0.05]	-3.58	1004	< .001
Edu num z	-0.08	[-0.14, -0.02]	-2.71	1004	.007
Age z	-0.25	[-0.31, -0.18]	-7.29	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(change_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-70)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.62	[-2.43, 1.18]	-0.68	966	.498
Simi openai z	-0.15	[-0.21, -0.09]	-4.96	966	< .001
Ideo con z	-0.13	[-0.20, -0.06]	-3.75	966	< .001
SDO z	-0.05	[-0.12, 0.02]	-1.43	966	.153
TIPI agree z	-0.01	[-0.07, 0.05]	-0.20	966	.839
Man	-0.30	[-0.42, -0.18]	-4.88	966	< .001
RaceAsian	0.74	[-1.09, 2.56]	0.79	966	.428
RaceBlack or African American	0.94	[-0.87, 2.75]	1.02	966	.307
RaceMiddle Eastern or North African	1.99	[-0.22, 4.20]	1.76	966	.078
Racemultiracial	0.80	[-1.03, 2.63]	0.86	966	.392
RaceNative Hawaiian or Other Pacific Islander	0.75	[-1.80, 3.30]	0.58	966	.562
RaceOther please specify	0.87	[-1.09, 2.83]	0.87	966	.383
RaceWhite	0.71	[-1.10, 2.51]	0.77	966	.443
Hispanic	0.14	[-0.09, 0.37]	1.18	966	.237
Income num z	-0.10	[-0.16, -0.04]	-3.21	966	.001
Edu num z	-0.08	[-0.14, -0.02]	-2.55	966	.011
Age z	-0.24	[-0.31, -0.17]	-6.92	966	< .001
County medianincome z	0.00	[-0.06, 0.06]	-0.04	966	.968
County gini z	-0.04	[-0.11, 0.03]	-1.19	966	.233
County density z	0.03	[-0.04, 0.10]	0.92	966	.356

Outcome Variable: Anti-establishment sentiment

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(antiest_z ~ simi_openai_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-71)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.06, 0.06]	-0.01	1120	.992
Simi openai z	-0.22	[-0.28, -0.16]	-7.64	1120	< .001
Ideo con z	-0.11	[-0.16, -0.05]	-3.71	1120	< .001

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(antiest_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-72)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.06, 0.06]	-0.01	1118	.994
Simi openai z	-0.21	[-0.27, -0.15]	-7.33	1118	< .001
Ideo con z	-0.13	[-0.19, -0.06]	-3.82	1118	< .001
SDO z	0.04	[-0.03, 0.11]	1.18	1118	.238
TIPI agree z	-0.08	[-0.14, -0.02]	-2.75	1118	.006

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(antiest_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-73)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.20	[-2.06, 1.67]	-0.21	1004	.837
Simi openai z	-0.21	[-0.27, -0.15]	-6.92	1004	< .001
Ideo con z	-0.12	[-0.19, -0.05]	-3.42	1004	< .001
SDO z	0.06	[-0.01, 0.13]	1.72	1004	.086
TIPI agree z	-0.07	[-0.13, -0.01]	-2.21	1004	.027
Man	0.01	[-0.11, 0.13]	0.22	1004	.829
RaceAsian	0.23	[-1.65, 2.11]	0.24	1004	.809
RaceBlack or African American	0.08	[-1.79, 1.94]	0.08	1004	.936
RaceMiddle Eastern or North African	0.18	[-2.11, 2.46]	0.15	1004	.880
Racemultiracial	0.01	[-1.87, 1.90]	0.02	1004	.988
RaceNative Hawaiian or Other Pacific Islander	0.19	[-2.44, 2.82]	0.14	1004	.889
RaceOther please specify	-0.15	[-2.15, 1.86]	-0.14	1004	.887
RaceWhite	0.19	[-1.67, 2.05]	0.20	1004	.840
Hispanic	0.02	[-0.22, 0.25]	0.15	1004	.878
Income num z	-0.13	[-0.19, -0.07]	-4.39	1004	< .001
Edu num z	-0.11	[-0.17, -0.05]	-3.50	1004	< .001
Age z	-0.12	[-0.18, -0.05]	-3.33	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(antiest_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-74)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.26	[-2.12, 1.61]	-0.27	966	.787
Simi openai z	-0.20	[-0.26, -0.14]	-6.69	966	< .001
Ideo con z	-0.13	[-0.20, -0.06]	-3.50	966	< .001
SDO z	0.07	[0.00, 0.14]	1.92	966	.055
TIPI agree z	-0.07	[-0.13, 0.00]	-2.10	966	.036
Man	0.01	[-0.11, 0.13]	0.15	966	.884
RaceAsian	0.30	[-1.58, 2.18]	0.31	966	.754
RaceBlack or African American	0.16	[-1.71, 2.03]	0.17	966	.865
RaceMiddle Eastern or North African	0.28	[-2.00, 2.57]	0.24	966	.807
Racemultiracial	0.08	[-1.81, 1.96]	0.08	966	.937
RaceNative Hawaiian or Other Pacific Islander	0.26	[-2.37, 2.89]	0.19	966	.847
RaceOther please specify	-0.02	[-2.04, 2.00]	-0.02	966	.983
RaceWhite	0.26	[-1.61, 2.12]	0.27	966	.788
Hispanic	0.04	[-0.19, 0.28]	0.36	966	.715
Income num z	-0.13	[-0.19, -0.07]	-4.18	966	< .001
Edu num z	-0.10	[-0.16, -0.04]	-3.10	966	.002
Age z	-0.12	[-0.19, -0.05]	-3.39	966	< .001
County medianincome z	-0.01	[-0.08, 0.05]	-0.34	966	.735
County gini z	-0.05	[-0.12, 0.03]	-1.29	966	.198
County density z	-0.01	[-0.08, 0.07]	-0.17	966	.863

Outcome Variable: Trust in democratic institutions

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(trust_deminst_z ~ simi_openai_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-75)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.05, 0.06]	0.11	1120	.914
Simi openai z	0.26	[0.20, 0.32]	9.04	1120	< .001
Ideo con z	0.06	[0.00, 0.12]	2.10	1120	.036

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(trust_deminst_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-76)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.05, 0.06]	0.12	1118	.908
Simi openai z	0.25	[0.19, 0.30]	8.65	1118	< .001
Ideo con z	0.10	[0.04, 0.17]	3.01	1118	.003
SDO z	-0.08	[-0.14, -0.01]	-2.26	1118	.024
TIPI agree z	0.10	[0.05, 0.16]	3.55	1118	< .001

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(trust_deminst_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-77)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.97	[-0.90, 2.84]	1.02	1004	.310
Simi openai z	0.23	[0.17, 0.29]	7.65	1004	< .001
Ideo con z	0.10	[0.03, 0.17]	2.87	1004	.004
SDO z	-0.09	[-0.16, -0.02]	-2.61	1004	.009
TIPI agree z	0.10	[0.04, 0.16]	3.18	1004	.002
Man	-0.02	[-0.14, 0.10]	-0.31	1004	.758
RaceAsian	-0.88	[-2.77, 1.01]	-0.92	1004	.360
RaceBlack or African American	-0.77	[-2.65, 1.11]	-0.81	1004	.421
RaceMiddle Eastern or North African	-1.62	[-3.92, 0.67]	-1.39	1004	.166
Racemultiracial	-0.96	[-2.86, 0.93]	-1.00	1004	.319
RaceNative Hawaiian or Other Pacific Islander	-0.70	[-3.35, 1.94]	-0.52	1004	.601
RaceOther please specify	-1.05	[-3.07, 0.97]	-1.02	1004	.308
RaceWhite	-0.95	[-2.83, 0.92]	-1.00	1004	.317
Hispanic	-0.07	[-0.31, 0.16]	-0.60	1004	.547
Income num z	0.05	[-0.01, 0.11]	1.73	1004	.085
Edu num z	0.11	[0.05, 0.17]	3.53	1004	< .001
Age z	0.01	[-0.05, 0.08]	0.42	1004	.676

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_deminst_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-78)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	1.08	[-0.78, 2.95]	1.14	966	.255
Simi openai z	0.23	[0.17, 0.29]	7.52	966	< .001
Ideo con z	0.11	[0.04, 0.18]	3.05	966	.002
SDO z	-0.10	[-0.17, -0.03]	-2.84	966	.005
TIPI agree z	0.10	[0.04, 0.16]	3.17	966	.002
Man	-0.02	[-0.15, 0.10]	-0.36	966	.716
RaceAsian	-1.00	[-2.88, 0.89]	-1.04	966	.299
RaceBlack or African American	-0.89	[-2.76, 0.98]	-0.94	966	.349
RaceMiddle Eastern or North African	-1.81	[-4.09, 0.48]	-1.55	966	.121
Racemultiracial	-1.07	[-2.96, 0.82]	-1.11	966	.266
RaceNative Hawaiian or Other Pacific Islander	-0.82	[-3.45, 1.81]	-0.61	966	.539
RaceOther please specify	-1.26	[-3.28, 0.76]	-1.22	966	.221
RaceWhite	-1.06	[-2.93, 0.80]	-1.12	966	.263
Hispanic	-0.07	[-0.31, 0.17]	-0.59	966	.557
Income num z	0.05	[-0.02, 0.11]	1.47	966	.142
Edu num z	0.09	[0.03, 0.15]	2.90	966	.004
Age z	0.03	[-0.04, 0.10]	0.81	966	.418
County medianincome z	0.03	[-0.04, 0.09]	0.81	966	.417
County gini z	0.03	[-0.04, 0.11]	0.84	966	.399
County density z	0.04	[-0.03, 0.12]	1.21	966	.227

Outcome Variable: Trust in national mainstream institutions

Model 1

Similarity score as predictor; conservatism as control.

m1 <- lm(trust_natinst_z ~ simi_openai_z + ideo_con_z ,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-79)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.07]	0.34	1120	.735
Simi openai z	0.22	[0.17, 0.28]	7.75	1120	< .001
Ideo con z	0.08	[0.03, 0.14]	2.92	1120	.004

Model 2

Similarity score as predictor; conservatism, SDO, and TIPI agreeableness as controls.

m1 <- lm(trust_natinst_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-80)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.06]	0.33	1118	.739
Simi openai z	0.21	[0.15, 0.26]	7.25	1118	< .001
Ideo con z	0.12	[0.06, 0.19]	3.75	1118	< .001
SDO z	-0.08	[-0.14, -0.01]	-2.24	1118	.025
TIPI agree z	0.18	[0.12, 0.24]	6.13	1118	< .001

Model 3

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, and age as controls.

m1 <- lm(trust_natinst_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-81)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.26	[-1.58, 2.09]	0.28	1004	.783
Simi openai z	0.20	[0.14, 0.25]	6.64	1004	< .001
Ideo con z	0.11	[0.04, 0.18]	3.24	1004	.001
SDO z	-0.09	[-0.15, -0.02]	-2.51	1004	.012
TIPI agree z	0.15	[0.09, 0.21]	4.80	1004	< .001
Man	0.02	[-0.10, 0.14]	0.36	1004	.720
RaceAsian	-0.15	[-2.00, 1.70]	-0.16	1004	.877
RaceBlack or African American	-0.09	[-1.93, 1.74]	-0.10	1004	.920
RaceMiddle Eastern or North African	-0.77	[-3.02, 1.48]	-0.67	1004	.504
Racemultiracial	-0.32	[-2.18, 1.54]	-0.34	1004	.733
RaceNative Hawaiian or Other Pacific Islander	0.50	[-2.09, 3.09]	0.38	1004	.707
RaceOther please specify	-0.58	[-2.55, 1.40]	-0.57	1004	.567
RaceWhite	-0.26	[-2.10, 1.57]	-0.28	1004	.778
Hispanic	-0.05	[-0.28, 0.18]	-0.45	1004	.656
Income num z	0.10	[0.04, 0.16]	3.31	1004	< .001
Edu num z	0.09	[0.03, 0.15]	3.01	1004	.003
Age z	0.17	[0.10, 0.24]	5.00	1004	< .001

Model 4

Similarity score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_natinst_z ~ simi_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-82)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.36	[-1.47, 2.18]	0.38	966	.702
Simi openai z	0.20	[0.14, 0.26]	6.61	966	< .001
Ideo con z	0.12	[0.05, 0.19]	3.32	966	< .001
SDO z	-0.10	[-0.17, -0.03]	-2.80	966	.005
TIPI agree z	0.15	[0.09, 0.21]	4.77	966	< .001
Man	0.03	[-0.09, 0.15]	0.49	966	.623
RaceAsian	-0.24	[-2.08, 1.60]	-0.25	966	.799
RaceBlack or African American	-0.20	[-2.03, 1.62]	-0.22	966	.828
RaceMiddle Eastern or North African	-0.94	[-3.17, 1.30]	-0.82	966	.411
Racemultiracial	-0.42	[-2.27, 1.42]	-0.45	966	.653
RaceNative Hawaiian or Other Pacific Islander	0.37	[-2.19, 2.94]	0.29	966	.775
RaceOther please specify	-0.79	[-2.76, 1.18]	-0.79	966	.431
RaceWhite	-0.36	[-2.18, 1.46]	-0.39	966	.695
Hispanic	-0.08	[-0.31, 0.16]	-0.64	966	.524
Income num z	0.09	[0.03, 0.15]	2.91	966	.004
Edu num z	0.08	[0.02, 0.14]	2.45	966	.014
Age z	0.18	[0.12, 0.25]	5.29	966	< .001
County medianincome z	0.04	[-0.03, 0.10]	1.16	966	.248
County gini z	-0.02	[-0.09, 0.05]	-0.48	966	.633
County density z	0.07	[0.00, 0.14]	2.08	966	.038

Interactions with political ideology

Outcome variable: Support for radical change

Conservatism

With controls

m1 <- lm(change_z ~ simi_openai_z*ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-83)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.63	[-2.44, 1.18]	-0.68	965	.495
Simi openai z	-0.15	[-0.21, -0.09]	-4.96	965	< .001
Ideo con z	-0.13	[-0.20, -0.06]	-3.68	965	< .001
SDO z	-0.05	[-0.11, 0.02]	-1.33	965	.184
TIPI agree z	0.00	[-0.06, 0.06]	-0.13	965	.899
Man	-0.30	[-0.42, -0.18]	-4.94	965	< .001
RaceAsian	0.75	[-1.08, 2.57]	0.80	965	.422
RaceBlack or African American	0.95	[-0.86, 2.76]	1.03	965	.303
RaceMiddle Eastern or North African	2.03	[-0.18, 4.25]	1.80	965	.072
Racemultiracial	0.80	[-1.02, 2.63]	0.86	965	.388
RaceNative Hawaiian or Other Pacific Islander	0.74	[-1.81, 3.29]	0.57	965	.569
RaceOther please specify	0.86	[-1.09, 2.82]	0.87	965	.387
RaceWhite	0.71	[-1.09, 2.52]	0.77	965	.440
Hispanic	0.14	[-0.09, 0.37]	1.17	965	.241
Income num z	-0.10	[-0.16, -0.04]	-3.27	965	.001
Edu num z	-0.07	[-0.14, -0.01]	-2.43	965	.015
Age z	-0.24	[-0.31, -0.17]	-6.90	965	< .001
County medianincome z	0.00	[-0.06, 0.06]	0.02	965	.986
County gini z	-0.04	[-0.11, 0.03]	-1.19	965	.233
County density z	0.03	[-0.04, 0.10]	0.91	965	.363
Simi openai z \(\times\) Ideo con z	0.04	[-0.02, 0.10]	1.32	965	.186

Without controls

m1 <- lm(change_z ~ simi_openai_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-84)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.03]	-0.77	1119	.440
Simi openai z	-0.16	[-0.21, -0.10]	-5.41	1119	< .001
Ideo con z	-0.19	[-0.25, -0.13]	-6.49	1119	< .001
Simi openai z \(\times\) Ideo con z	0.03	[-0.03, 0.09]	1.08	1119	.282

Liberalism

With controls

m1 <- lm(change_z ~ simi_openai_z*ideo_lib_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-85)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.43	[-2.24, 1.38]	-0.47	959	.639
Simi openai z	-0.17	[-0.22, -0.11]	-5.51	959	< .001
Ideo lib z	0.04	[-0.02, 0.11]	1.30	959	.194
SDO z	-0.10	[-0.17, -0.04]	-3.07	959	.002
TIPI agree z	-0.01	[-0.07, 0.05]	-0.39	959	.699
Man	-0.31	[-0.43, -0.19]	-5.05	959	< .001
RaceAsian	0.61	[-1.22, 2.43]	0.65	959	.513
RaceBlack or African American	0.77	[-1.04, 2.58]	0.83	959	.406
RaceMiddle Eastern or North African	1.64	[-0.57, 3.85]	1.45	959	.146
Racemultiracial	0.59	[-1.24, 2.42]	0.63	959	.527
RaceNative Hawaiian or Other Pacific Islander	0.64	[-1.91, 3.20]	0.49	959	.621
RaceOther please specify	0.75	[-1.21, 2.70]	0.75	959	.455
RaceWhite	0.51	[-1.30, 2.31]	0.55	959	.582
Hispanic	0.21	[-0.02, 0.45]	1.81	959	.070
Income num z	-0.09	[-0.15, -0.03]	-3.01	959	.003
Edu num z	-0.08	[-0.14, -0.02]	-2.54	959	.011
Age z	-0.24	[-0.31, -0.17]	-6.87	959	< .001
County medianincome z	0.01	[-0.05, 0.07]	0.26	959	.794
County gini z	-0.03	[-0.10, 0.04]	-0.79	959	.430
County density z	0.02	[-0.05, 0.09]	0.63	959	.530
Simi openai z \(\times\) Ideo lib z	-0.01	[-0.07, 0.04]	-0.50	959	.619

Without controls

m1 <- lm(change_z ~ simi_openai_z*ideo_lib_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-86)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.04]	-0.63	1114	.531
Simi openai z	-0.17	[-0.23, -0.11]	-5.79	1114	< .001
Ideo lib z	0.11	[0.05, 0.17]	3.82	1114	< .001
Simi openai z \(\times\) Ideo lib z	0.01	[-0.04, 0.07]	0.44	1114	.662

Democratic socialism

With controls

m1 <- lm(change_z ~ simi_openai_z*ideo_demsoc_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-87)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.20	[-0.08, 0.49]	1.40	914	.163
Simi openai z	-0.17	[-0.23, -0.11]	-5.54	914	< .001
Ideo demsoc z	0.17	[0.10, 0.23]	4.89	914	< .001
SDO z	-0.05	[-0.11, 0.02]	-1.40	914	.161
TIPI agree z	0.00	[-0.06, 0.06]	0.07	914	.946
Man	-0.30	[-0.42, -0.18]	-4.83	914	< .001
RaceBlack or African American	0.05	[-0.27, 0.36]	0.30	914	.765
RaceMiddle Eastern or North African	0.85	[-0.45, 2.15]	1.29	914	.198
Racemultiracial	-0.01	[-0.43, 0.42]	-0.03	914	.976
RaceNative Hawaiian or Other Pacific Islander	-0.27	[-2.08, 1.54]	-0.29	914	.769
RaceOther please specify	0.08	[-0.71, 0.87]	0.19	914	.847
RaceWhite	-0.11	[-0.40, 0.18]	-0.74	914	.460
Hispanic	0.15	[-0.08, 0.38]	1.26	914	.207
Income num z	-0.10	[-0.16, -0.04]	-3.21	914	.001
Edu num z	-0.11	[-0.17, -0.05]	-3.39	914	< .001
Age z	-0.24	[-0.31, -0.17]	-6.87	914	< .001
County medianincome z	0.02	[-0.04, 0.09]	0.73	914	.466
County gini z	-0.02	[-0.09, 0.05]	-0.55	914	.581
County density z	0.02	[-0.05, 0.09]	0.47	914	.641
Simi openai z \(\times\) Ideo demsoc z	-0.03	[-0.09, 0.03]	-0.87	914	.384

Without controls

m1 <- lm(change_z ~ simi_openai_z*ideo_demsoc_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-88)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.02	[-0.08, 0.04]	-0.62	1061	.538
Simi openai z	-0.17	[-0.23, -0.11]	-5.82	1061	< .001
Ideo demsoc z	0.23	[0.17, 0.29]	7.74	1061	< .001
Simi openai z \(\times\) Ideo demsoc z	0.01	[-0.05, 0.07]	0.37	1061	.713

Libertarianism

With controls

m1 <- lm(change_z ~ simi_openai_z*ideo_lbrtn_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-89)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.49	[-2.31, 1.33]	-0.53	876	.599
Simi openai z	-0.16	[-0.22, -0.10]	-5.16	876	< .001
Ideo lbrtn z	-0.04	[-0.10, 0.02]	-1.31	876	.191
SDO z	-0.11	[-0.18, -0.05]	-3.54	876	< .001
TIPI agree z	-0.01	[-0.07, 0.06]	-0.18	876	.857
Man	-0.28	[-0.40, -0.15]	-4.24	876	< .001
RaceAsian	0.69	[-1.15, 2.53]	0.74	876	.462
RaceBlack or African American	0.78	[-1.05, 2.61]	0.84	876	.401
RaceMiddle Eastern or North African	1.81	[-0.42, 4.05]	1.59	876	.112
Racemultiracial	0.68	[-1.17, 2.53]	0.72	876	.470
RaceNative Hawaiian or Other Pacific Islander	0.87	[-1.71, 3.44]	0.66	876	.510
RaceOther please specify	0.51	[-1.49, 2.51]	0.50	876	.617
RaceWhite	0.53	[-1.29, 2.35]	0.57	876	.571
Hispanic	0.20	[-0.04, 0.44]	1.64	876	.102
Income num z	-0.11	[-0.17, -0.05]	-3.38	876	< .001
Edu num z	-0.09	[-0.15, -0.02]	-2.65	876	.008
Age z	-0.24	[-0.32, -0.17]	-6.51	876	< .001
County medianincome z	0.01	[-0.05, 0.08]	0.43	876	.668
County gini z	-0.03	[-0.11, 0.05]	-0.78	876	.433
County density z	0.03	[-0.05, 0.10]	0.72	876	.474
Simi openai z \(\times\) Ideo lbrtn z	0.05	[-0.01, 0.11]	1.70	876	.089

Without controls

m1 <- lm(change_z ~ simi_openai_z*ideo_lbrtn_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-90)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.06	[-0.12, 0.01]	-1.78	1014	.076
Simi openai z	-0.16	[-0.22, -0.10]	-5.28	1014	< .001
Ideo lbrtn z	-0.05	[-0.11, 0.01]	-1.57	1014	.116
Simi openai z \(\times\) Ideo lbrtn z	0.07	[0.01, 0.13]	2.35	1014	.019

Progressivism

With controls

m1 <- lm(change_z ~ simi_openai_z*ideo_prog_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-91)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.39	[-2.20, 1.42]	-0.42	921	.674
Simi openai z	-0.17	[-0.23, -0.11]	-5.44	921	< .001
Ideo prog z	0.13	[0.06, 0.19]	3.62	921	< .001
SDO z	-0.07	[-0.14, 0.00]	-1.97	921	.049
TIPI agree z	-0.01	[-0.07, 0.05]	-0.35	921	.728
Man	-0.30	[-0.42, -0.18]	-4.77	921	< .001
RaceAsian	0.55	[-1.28, 2.38]	0.59	921	.556
RaceBlack or African American	0.71	[-1.10, 2.53]	0.77	921	.441
RaceMiddle Eastern or North African	1.68	[-0.54, 3.90]	1.49	921	.137
Racemultiracial	0.59	[-1.24, 2.43]	0.63	921	.526
RaceNative Hawaiian or Other Pacific Islander	0.47	[-2.09, 3.03]	0.36	921	.720
RaceOther please specify	0.69	[-1.27, 2.65]	0.69	921	.491
RaceWhite	0.47	[-1.34, 2.28]	0.51	921	.610
Hispanic	0.16	[-0.07, 0.39]	1.36	921	.174
Income num z	-0.09	[-0.15, -0.03]	-2.89	921	.004
Edu num z	-0.10	[-0.16, -0.04]	-3.14	921	.002
Age z	-0.25	[-0.32, -0.18]	-6.87	921	< .001
County medianincome z	0.01	[-0.05, 0.08]	0.42	921	.675
County gini z	-0.03	[-0.10, 0.04]	-0.78	921	.433
County density z	0.03	[-0.04, 0.10]	0.77	921	.441
Simi openai z \(\times\) Ideo prog z	0.01	[-0.04, 0.07]	0.47	921	.639

Without controls

m1 <- lm(change_z ~ simi_openai_z*ideo_prog_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-92)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.03	[-0.09, 0.03]	-1.00	1074	.320
Simi openai z	-0.18	[-0.24, -0.12]	-5.95	1074	< .001
Ideo prog z	0.19	[0.13, 0.25]	6.43	1074	< .001
Simi openai z \(\times\) Ideo prog z	0.04	[-0.01, 0.10]	1.46	1074	.144

Outcome variable: Anti-establishment

Conservatism

With controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-93)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.26	[-2.13, 1.60]	-0.28	965	.782
Simi openai z	-0.21	[-0.27, -0.14]	-6.70	965	< .001
Ideo con z	-0.12	[-0.20, -0.05]	-3.43	965	< .001
SDO z	0.07	[0.00, 0.14]	2.02	965	.044
TIPI agree z	-0.06	[-0.13, 0.00]	-2.02	965	.044
Man	0.00	[-0.12, 0.13]	0.07	965	.940
RaceAsian	0.31	[-1.57, 2.19]	0.33	965	.745
RaceBlack or African American	0.17	[-1.70, 2.04]	0.18	965	.858
RaceMiddle Eastern or North African	0.33	[-1.95, 2.62]	0.28	965	.776
Racemultiracial	0.08	[-1.80, 1.97]	0.09	965	.931
RaceNative Hawaiian or Other Pacific Islander	0.25	[-2.38, 2.87]	0.18	965	.854
RaceOther please specify	-0.03	[-2.05, 1.99]	-0.03	965	.978
RaceWhite	0.26	[-1.60, 2.12]	0.27	965	.784
Hispanic	0.04	[-0.19, 0.28]	0.35	965	.723
Income num z	-0.13	[-0.20, -0.07]	-4.25	965	< .001
Edu num z	-0.09	[-0.16, -0.03]	-2.97	965	.003
Age z	-0.12	[-0.19, -0.05]	-3.37	965	< .001
County medianincome z	-0.01	[-0.07, 0.06]	-0.28	965	.781
County gini z	-0.05	[-0.12, 0.03]	-1.29	965	.198
County density z	-0.01	[-0.08, 0.06]	-0.19	965	.852
Simi openai z \(\times\) Ideo con z	0.04	[-0.02, 0.10]	1.39	965	.163

Without controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-94)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.06, 0.06]	-0.05	1119	.963
Simi openai z	-0.22	[-0.28, -0.16]	-7.61	1119	< .001
Ideo con z	-0.10	[-0.16, -0.05]	-3.60	1119	< .001
Simi openai z \(\times\) Ideo con z	0.04	[-0.02, 0.10]	1.42	1119	.155

Liberalism

With controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_lib_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-95)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.04	[-1.90, 1.82]	-0.04	959	.968
Simi openai z	-0.22	[-0.28, -0.16]	-7.14	959	< .001
Ideo lib z	-0.04	[-0.11, 0.02]	-1.26	959	.208
SDO z	-0.02	[-0.09, 0.05]	-0.65	959	.516
TIPI agree z	-0.08	[-0.14, -0.02]	-2.49	959	.013
Man	-0.03	[-0.15, 0.10]	-0.45	959	.656
RaceAsian	0.18	[-1.70, 2.05]	0.19	959	.852
RaceBlack or African American	0.01	[-1.86, 1.87]	0.01	959	.995
RaceMiddle Eastern or North African	0.02	[-2.25, 2.30]	0.02	959	.984
Racemultiracial	-0.14	[-2.03, 1.74]	-0.15	959	.882
RaceNative Hawaiian or Other Pacific Islander	0.26	[-2.37, 2.89]	0.19	959	.846
RaceOther please specify	-0.17	[-2.18, 1.85]	-0.16	959	.871
RaceWhite	0.05	[-1.81, 1.91]	0.05	959	.958
Hispanic	0.07	[-0.17, 0.31]	0.57	959	.567
Income num z	-0.14	[-0.21, -0.08]	-4.53	959	< .001
Edu num z	-0.11	[-0.17, -0.04]	-3.28	959	.001
Age z	-0.12	[-0.19, -0.05]	-3.32	959	< .001
County medianincome z	0.00	[-0.06, 0.07]	0.11	959	.915
County gini z	-0.03	[-0.10, 0.05]	-0.67	959	.500
County density z	-0.02	[-0.09, 0.05]	-0.66	959	.512
Simi openai z \(\times\) Ideo lib z	-0.02	[-0.08, 0.04]	-0.60	959	.552

Without controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_lib_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-96)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.06]	0.21	1114	.831
Simi openai z	-0.23	[-0.29, -0.17]	-7.89	1114	< .001
Ideo lib z	-0.05	[-0.10, 0.01]	-1.64	1114	.101
Simi openai z \(\times\) Ideo lib z	-0.01	[-0.07, 0.04]	-0.51	1114	.609

Democratic socialism

With controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_demsoc_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-97)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.18	[-0.12, 0.48]	1.20	914	.232
Simi openai z	-0.22	[-0.28, -0.16]	-7.08	914	< .001
Ideo demsoc z	0.04	[-0.03, 0.11]	1.15	914	.250
SDO z	0.03	[-0.04, 0.10]	0.83	914	.404
TIPI agree z	-0.07	[-0.13, 0.00]	-2.00	914	.045
Man	-0.01	[-0.14, 0.11]	-0.20	914	.841
RaceBlack or African American	-0.30	[-0.62, 0.03]	-1.78	914	.075
RaceMiddle Eastern or North African	-0.26	[-1.61, 1.09]	-0.38	914	.704
Racemultiracial	-0.33	[-0.76, 0.11]	-1.47	914	.143
RaceNative Hawaiian or Other Pacific Islander	-0.14	[-2.01, 1.74]	-0.14	914	.887
RaceOther please specify	-0.42	[-1.24, 0.39]	-1.02	914	.309
RaceWhite	-0.16	[-0.46, 0.15]	-1.01	914	.313
Hispanic	0.02	[-0.22, 0.27]	0.19	914	.848
Income num z	-0.14	[-0.21, -0.08]	-4.34	914	< .001
Edu num z	-0.11	[-0.17, -0.04]	-3.27	914	.001
Age z	-0.14	[-0.21, -0.06]	-3.73	914	< .001
County medianincome z	0.01	[-0.05, 0.08]	0.44	914	.660
County gini z	-0.02	[-0.09, 0.05]	-0.53	914	.593
County density z	-0.03	[-0.10, 0.04]	-0.82	914	.411
Simi openai z \(\times\) Ideo demsoc z	-0.04	[-0.10, 0.02]	-1.19	914	.235

Without controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_demsoc_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-98)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.07]	0.33	1061	.739
Simi openai z	-0.23	[-0.28, -0.17]	-7.74	1061	< .001
Ideo demsoc z	0.04	[-0.02, 0.09]	1.19	1061	.233
Simi openai z \(\times\) Ideo demsoc z	-0.02	[-0.08, 0.04]	-0.74	1061	.461

Libertarianism

With controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_lbrtn_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-99)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.03	[-1.87, 1.81]	-0.03	876	.973
Simi openai z	-0.23	[-0.29, -0.17]	-7.28	876	< .001
Ideo lbrtn z	0.00	[-0.07, 0.06]	-0.13	876	.893
SDO z	0.01	[-0.06, 0.07]	0.21	876	.836
TIPI agree z	-0.07	[-0.13, -0.01]	-2.12	876	.035
Man	-0.01	[-0.14, 0.11]	-0.23	876	.822
RaceAsian	0.25	[-1.61, 2.11]	0.26	876	.795
RaceBlack or African American	-0.08	[-1.93, 1.77]	-0.08	876	.934
RaceMiddle Eastern or North African	0.03	[-2.23, 2.29]	0.03	876	.978
Racemultiracial	-0.13	[-2.00, 1.74]	-0.14	876	.892
RaceNative Hawaiian or Other Pacific Islander	0.17	[-2.43, 2.78]	0.13	876	.897
RaceOther please specify	-0.31	[-2.34, 1.71]	-0.30	876	.762
RaceWhite	0.06	[-1.78, 1.90]	0.06	876	.953
Hispanic	0.03	[-0.21, 0.27]	0.26	876	.797
Income num z	-0.14	[-0.20, -0.08]	-4.28	876	< .001
Edu num z	-0.11	[-0.18, -0.05]	-3.42	876	< .001
Age z	-0.14	[-0.21, -0.06]	-3.57	876	< .001
County medianincome z	0.02	[-0.04, 0.09]	0.67	876	.502
County gini z	-0.04	[-0.12, 0.03]	-1.07	876	.287
County density z	-0.02	[-0.10, 0.05]	-0.60	876	.552
Simi openai z \(\times\) Ideo lbrtn z	0.00	[-0.06, 0.06]	-0.03	876	.973

Without controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_lbrtn_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-100)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.06, 0.06]	0.07	1014	.941
Simi openai z	-0.24	[-0.29, -0.18]	-7.88	1014	< .001
Ideo lbrtn z	-0.03	[-0.09, 0.03]	-0.86	1014	.391
Simi openai z \(\times\) Ideo lbrtn z	-0.01	[-0.06, 0.05]	-0.18	1014	.860

Progressivism

With controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_prog_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-101)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.06	[-1.91, 1.80]	-0.06	921	.953
Simi openai z	-0.23	[-0.29, -0.17]	-7.40	921	< .001
Ideo prog z	-0.04	[-0.11, 0.03]	-1.13	921	.258
SDO z	-0.01	[-0.08, 0.06]	-0.28	921	.780
TIPI agree z	-0.08	[-0.14, -0.01]	-2.40	921	.017
Man	-0.03	[-0.15, 0.10]	-0.39	921	.694
RaceAsian	0.26	[-1.60, 2.13]	0.28	921	.781
RaceBlack or African American	0.00	[-1.85, 1.86]	0.00	921	.998
RaceMiddle Eastern or North African	0.02	[-2.25, 2.28]	0.01	921	.989
Racemultiracial	-0.06	[-1.94, 1.82]	-0.06	921	.951
RaceNative Hawaiian or Other Pacific Islander	0.26	[-2.36, 2.87]	0.19	921	.847
RaceOther please specify	-0.18	[-2.19, 1.82]	-0.18	921	.859
RaceWhite	0.08	[-1.77, 1.93]	0.08	921	.934
Hispanic	0.06	[-0.18, 0.29]	0.47	921	.642
Income num z	-0.14	[-0.20, -0.07]	-4.25	921	< .001
Edu num z	-0.10	[-0.17, -0.04]	-3.14	921	.002
Age z	-0.14	[-0.21, -0.07]	-3.76	921	< .001
County medianincome z	0.01	[-0.05, 0.08]	0.34	921	.733
County gini z	-0.04	[-0.12, 0.03]	-1.09	921	.276
County density z	-0.02	[-0.09, 0.06]	-0.40	921	.690
Simi openai z \(\times\) Ideo prog z	-0.03	[-0.09, 0.03]	-0.85	921	.395

Without controls

m1 <- lm(antiest_z ~ simi_openai_z*ideo_prog_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-102)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.01	[-0.05, 0.07]	0.38	1074	.706
Simi openai z	-0.24	[-0.29, -0.18]	-8.02	1074	< .001
Ideo prog z	-0.03	[-0.09, 0.03]	-0.93	1074	.353
Simi openai z \(\times\) Ideo prog z	-0.01	[-0.07, 0.05]	-0.33	1074	.743

Outcome variable: Trust in democratic institutions

Conservatism

With controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_con_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-103)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	1.08	[-0.78, 2.95]	1.14	965	.255
Simi openai z	0.23	[0.17, 0.29]	7.52	965	< .001
Ideo con z	0.11	[0.04, 0.18]	3.03	965	.003
SDO z	-0.10	[-0.17, -0.03]	-2.87	965	.004
TIPI agree z	0.10	[0.04, 0.16]	3.14	965	.002
Man	-0.02	[-0.15, 0.10]	-0.34	965	.733
RaceAsian	-1.00	[-2.88, 0.88]	-1.04	965	.298
RaceBlack or African American	-0.90	[-2.76, 0.97]	-0.94	965	.347
RaceMiddle Eastern or North African	-1.82	[-4.11, 0.46]	-1.57	965	.118
Racemultiracial	-1.07	[-2.96, 0.81]	-1.12	965	.265
RaceNative Hawaiian or Other Pacific Islander	-0.82	[-3.45, 1.81]	-0.61	965	.541
RaceOther please specify	-1.26	[-3.27, 0.76]	-1.22	965	.222
RaceWhite	-1.06	[-2.93, 0.80]	-1.12	965	.262
Hispanic	-0.07	[-0.31, 0.17]	-0.58	965	.559
Income num z	0.05	[-0.02, 0.11]	1.49	965	.137
Edu num z	0.09	[0.03, 0.15]	2.85	965	.005
Age z	0.03	[-0.04, 0.10]	0.80	965	.423
County medianincome z	0.03	[-0.04, 0.09]	0.79	965	.428
County gini z	0.03	[-0.04, 0.11]	0.84	965	.399
County density z	0.04	[-0.03, 0.12]	1.21	965	.226
Simi openai z \(\times\) Ideo con z	-0.01	[-0.07, 0.05]	-0.43	965	.667

Without controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-104)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.00	[-0.05, 0.06]	0.14	1119	.887
Simi openai z	0.26	[0.20, 0.32]	9.02	1119	< .001
Ideo con z	0.06	[0.00, 0.11]	2.00	1119	.046
Simi openai z \(\times\) Ideo con z	-0.04	[-0.09, 0.02]	-1.34	1119	.182

Liberalism

With controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_lib_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-105)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.93	[-0.92, 2.79]	0.99	959	.324
Simi openai z	0.24	[0.18, 0.30]	7.71	959	< .001
Ideo lib z	0.11	[0.04, 0.18]	3.20	959	.001
SDO z	0.01	[-0.05, 0.08]	0.42	959	.677
TIPI agree z	0.11	[0.05, 0.18]	3.53	959	< .001
Man	-0.01	[-0.13, 0.11]	-0.17	959	.862
RaceAsian	-0.92	[-2.79, 0.96]	-0.96	959	.338
RaceBlack or African American	-0.80	[-2.66, 1.06]	-0.84	959	.400
RaceMiddle Eastern or North African	-1.65	[-3.92, 0.62]	-1.42	959	.155
Racemultiracial	-0.93	[-2.81, 0.95]	-0.97	959	.333
RaceNative Hawaiian or Other Pacific Islander	-0.99	[-3.61, 1.64]	-0.74	959	.461
RaceOther please specify	-1.14	[-3.15, 0.87]	-1.11	959	.268
RaceWhite	-0.91	[-2.77, 0.94]	-0.96	959	.335
Hispanic	-0.13	[-0.36, 0.11]	-1.03	959	.301
Income num z	0.06	[-0.01, 0.12]	1.79	959	.074
Edu num z	0.08	[0.02, 0.14]	2.55	959	.011
Age z	0.02	[-0.05, 0.09]	0.57	959	.570
County medianincome z	0.01	[-0.06, 0.07]	0.18	959	.856
County gini z	0.01	[-0.07, 0.08]	0.17	959	.862
County density z	0.06	[-0.01, 0.13]	1.71	959	.088
Simi openai z \(\times\) Ideo lib z	0.01	[-0.05, 0.07]	0.20	959	.840

Without controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_lib_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-106)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.01	[-0.07, 0.05]	-0.32	1114	.747
Simi openai z	0.26	[0.21, 0.32]	9.10	1114	< .001
Ideo lib z	0.12	[0.07, 0.18]	4.24	1114	< .001
Simi openai z \(\times\) Ideo lib z	0.03	[-0.03, 0.08]	0.97	1114	.333

Democratic socialism

With controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_demsoc_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-107)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.07	[-0.37, 0.23]	-0.47	914	.638
Simi openai z	0.25	[0.19, 0.32]	8.16	914	< .001
Ideo demsoc z	0.04	[-0.02, 0.11]	1.26	914	.207
SDO z	-0.04	[-0.11, 0.03]	-1.01	914	.311
TIPI agree z	0.09	[0.03, 0.16]	2.85	914	.005
Man	-0.01	[-0.13, 0.12]	-0.12	914	.902
RaceBlack or African American	0.23	[-0.09, 0.56]	1.40	914	.161
RaceMiddle Eastern or North African	-0.63	[-1.98, 0.71]	-0.92	914	.355
Racemultiracial	0.05	[-0.39, 0.49]	0.23	914	.819
RaceNative Hawaiian or Other Pacific Islander	0.20	[-1.67, 2.07]	0.21	914	.831
RaceOther please specify	-0.13	[-0.94, 0.69]	-0.30	914	.763
RaceWhite	0.09	[-0.22, 0.39]	0.56	914	.578
Hispanic	-0.11	[-0.35, 0.14]	-0.86	914	.390
Income num z	0.05	[-0.01, 0.12]	1.65	914	.099
Edu num z	0.09	[0.03, 0.16]	2.75	914	.006
Age z	0.04	[-0.03, 0.11]	1.06	914	.288
County medianincome z	0.00	[-0.06, 0.07]	0.11	914	.911
County gini z	-0.01	[-0.09, 0.06]	-0.31	914	.757
County density z	0.09	[0.01, 0.16]	2.34	914	.019
Simi openai z \(\times\) Ideo demsoc z	0.08	[0.02, 0.14]	2.54	914	.011

Without controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_demsoc_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-108)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.01	[-0.07, 0.05]	-0.26	1061	.792
Simi openai z	0.27	[0.22, 0.33]	9.37	1061	< .001
Ideo demsoc z	0.07	[0.01, 0.13]	2.35	1061	.019
Simi openai z \(\times\) Ideo demsoc z	0.08	[0.02, 0.14]	2.76	1061	.006

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_demsoc_z,data = df_bsc_elg)

interact_plot(m1,
              pred = simi_openai_z,
              modx = ideo_demsoc_z,
              interval = T)

Libertarianism

With controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_lbrtn_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-110)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.90	[-0.96, 2.76]	0.95	876	.340
Simi openai z	0.26	[0.20, 0.32]	8.17	876	< .001
Ideo lbrtn z	0.02	[-0.04, 0.09]	0.67	876	.502
SDO z	-0.04	[-0.10, 0.03]	-1.08	876	.279
TIPI agree z	0.10	[0.04, 0.17]	3.14	876	.002
Man	-0.03	[-0.16, 0.10]	-0.49	876	.623
RaceAsian	-1.00	[-2.88, 0.87]	-1.05	876	.294
RaceBlack or African American	-0.70	[-2.56, 1.16]	-0.74	876	.461
RaceMiddle Eastern or North African	-1.57	[-3.85, 0.71]	-1.35	876	.176
Racemultiracial	-0.96	[-2.84, 0.93]	-1.00	876	.319
RaceNative Hawaiian or Other Pacific Islander	-0.73	[-3.36, 1.90]	-0.55	876	.584
RaceOther please specify	-0.99	[-3.03, 1.05]	-0.95	876	.342
RaceWhite	-0.89	[-2.75, 0.96]	-0.94	876	.346
Hispanic	-0.08	[-0.32, 0.17]	-0.63	876	.529
Income num z	0.06	[-0.01, 0.12]	1.78	876	.075
Edu num z	0.10	[0.04, 0.17]	3.02	876	.003
Age z	0.04	[-0.03, 0.12]	1.05	876	.293
County medianincome z	-0.01	[-0.08, 0.06]	-0.21	876	.830
County gini z	0.00	[-0.08, 0.08]	0.02	876	.984
County density z	0.09	[0.01, 0.16]	2.23	876	.026
Simi openai z \(\times\) Ideo lbrtn z	0.03	[-0.03, 0.09]	1.01	876	.311

Without controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_lbrtn_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-111)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.01	[-0.07, 0.05]	-0.37	1014	.712
Simi openai z	0.28	[0.22, 0.34]	9.42	1014	< .001
Ideo lbrtn z	0.05	[-0.01, 0.11]	1.59	1014	.113
Simi openai z \(\times\) Ideo lbrtn z	0.04	[-0.01, 0.10]	1.51	1014	.131

Progressivism

With controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_prog_z + SDO_z + TIPI_agree_z + man + race + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-112)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	0.95	[-0.91, 2.81]	1.00	921	.316
Simi openai z	0.25	[0.19, 0.31]	7.95	921	< .001
Ideo prog z	0.11	[0.04, 0.18]	3.19	921	.001
SDO z	0.01	[-0.06, 0.08]	0.24	921	.809
TIPI agree z	0.10	[0.04, 0.17]	3.18	921	.002
Man	-0.02	[-0.15, 0.11]	-0.30	921	.765
RaceAsian	-1.00	[-2.88, 0.88]	-1.05	921	.296
RaceBlack or African American	-0.83	[-2.70, 1.03]	-0.88	921	.381
RaceMiddle Eastern or North African	-1.55	[-3.83, 0.73]	-1.34	921	.182
Racemultiracial	-1.00	[-2.88, 0.89]	-1.04	921	.300
RaceNative Hawaiian or Other Pacific Islander	-0.97	[-3.60, 1.66]	-0.72	921	.469
RaceOther please specify	-1.13	[-3.15, 0.88]	-1.10	921	.270
RaceWhite	-0.92	[-2.78, 0.94]	-0.97	921	.331
Hispanic	-0.14	[-0.38, 0.10]	-1.18	921	.238
Income num z	0.05	[-0.01, 0.12]	1.62	921	.106
Edu num z	0.09	[0.02, 0.15]	2.70	921	.007
Age z	0.03	[-0.04, 0.10]	0.79	921	.432
County medianincome z	0.01	[-0.06, 0.07]	0.26	921	.798
County gini z	0.01	[-0.07, 0.08]	0.18	921	.858
County density z	0.06	[-0.01, 0.14]	1.70	921	.090
Simi openai z \(\times\) Ideo prog z	0.02	[-0.04, 0.08]	0.59	921	.553

Without controls

m1 <- lm(trust_deminst_z ~ simi_openai_z*ideo_prog_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)

(#tab:unnamed-chunk-113)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-0.01	[-0.07, 0.04]	-0.45	1074	.653
Simi openai z	0.27	[0.21, 0.33]	9.21	1074	< .001
Ideo prog z	0.11	[0.05, 0.17]	3.80	1074	< .001
Simi openai z \(\times\) Ideo prog z	0.03	[-0.02, 0.09]	1.17	1074	.244