Speech style and education distinguish the grammatical classes of (ING)
Sarah Horwitz
Introduction
The stability and salience of variable (ING) (workin’ ~ working) (Labov 2001a:86) have foregrounded (ING)’s role in the study of language variation and change (Fischer 1958, Labov 1966/1982, Trudgill 1974, Campbell-Kibler 2007, Houston 1985), yet we have limited knowledge of (ING)’s cross-varietal patterns of usage (Tagliamonte 2004:394). We analyze constraints on (ING) across grammatical category in Philadelphia English and show that nominal, verbal and quantifier (ING) are conditioned differently. We argue that (ING)’s variant stylistic conditioning supports a conception of (ING) as more than one variable and venture that (ING) may be less stable than is widely believed. In demonstrating the sensitivity of sub-surface aspects of (ING) to external factors like style, this study bears on the value of nuanced investigations. As a larger scale corpus study, this work also represents a convergence between the findings of corpus studies and the findings of qualitative (references) and experimental (references) work on (ING)’s indexicality.
Despite the -ing suffix’s superficial uniformity, there is evidence that nominal and verbal (ING) evolved from discrete sources (Tagliamonte 2004), making the divergent behavior of these grammatical forms less surprising. -ing resulted from the combination of at least two grammatical forms: the present participle, which in Old English was -ende, and the verbal noun, which in Old English was formed with the -ung suffix. In Middle English, through a sequence of reduction processes -end gradually replaced -ende, from which point it evolved to -en and finally to -in. Concurrently, spellings of “ing” or “ynge” began to replace the Old English -ung spelling. An explosion of variation in usage of (ING), connected to social evaluation, occurred once -ing and -in became commonly understood as variants of the same (ING) suffix. Our study probes one of the factors that emerged to condition variation within (ING), style. We seek to determine if the rate of (ING) usage is sensitive to style, and test the hypothesis that (ING) usage is indeed sensitive to variation across stylistic contexts.
Data
Data come from a roughly age/gender-balanced sample of 40 speakers from the Philadelphia Neighborhood Corpus (Labov & Rosenfelder 2011). Every instance of (ING) was coded for pronunciation using a binary system of classification consisting of “0s” or “1s”, with the “0” code corresponding to the nonstandard apical [ɪn] variant, and the “1” code corresponding to the standard velar [ɪŋ] variant. Each instance of (ING) was also coded for grammatical class: nominal (including monomorphemes and nominal gerunds; n = 527), verbal (participles and progressives; n = 391) or quantifier (something/nothing; n = 126). The preceding segments (velar-nasal, coronal-nasal, coronal-obstruent, or other) and following segments (velar, pause, or other) were accounted for, and were coded based on their place and manner of articulation.
We follow the model for grammatical coding of (ING) developed by Tamminga (2014:44-49). Monomorphemic (ING) refers to roots, or items that are stored as whole objects in the lexicon.
Monomorphemic (ING): (1) Good morning! (2) Watch out for the low ceiling.
Nominal gerunds refer to words that can be split into a nominal or adjectival root and an -ing suffix, but whose meanings derive from both parts of the word.
Nominal gerunds: (1) What a magnificent apartment building! (2) The fashionista wears stylish clothing.
Participles, like progressives, are words partially composed of a verbal head. Unlike progressives, however, participles can be substituted with nouns.
Participles: (1) She does computer coding (akin to “She does computer work”).
Progressives: (1) The winner was jumping for joy.
Finally, something/nothing quantifiers are notable because they can be articulated in more reduced ways, with glottal stops and syllabic nasals, in addition to being realized with (ING)’s common apical and velar variants. Although we acknowledge the existence of these reduced articulations, we have excluded them from our data in order to focus on variation across (ING)’s apical and velar forms.
Stylistic variation across (ING)’s grammatical categories
## slightly confused about index values on graph? still working on it
# Working in dplyr
library(dplyr)
# Working in ggplot (graphing)
library(ggplot2)
# Acessing data file
ing.lsa2 <- read.csv("ing.lsa2.csv")
# Turning ing.lsa2 into dplyr dataframe
ing.lsa2 <- tbl_df(ing.lsa2)
# Naming the dataframe
ing.lsa2 <- ing.lsa2 %>%
# Creating data in long format
group_by(Bin_style, newgram2) %>%
# Summarize; add the ret rate and N column
summarise(index=mean(code),N=n())
# Making graph of grammatically differentiated (ing) rate across stylistic category
ggplot(ing.lsa2, aes(newgram2,index)) +
geom_bar(stat = "identity", aes(fill=Bin_style), position = "dodge") +
ggtitle("Style-shifting in (ING) across grammatical class") +
labs(x = "Grammatical Class", y = "Rate of velar (ING)",
title = "Style-shifting in (ING) across grammatical class",
fill = "Style")
Analysis
We adopt Labov (2001b)’s Style Decision Tree for classifying speech in the sociolinguistic interview into 1 of 8 contextual styles, further grouped into “Careful” or “Casual” for analysis. We expect that more [ɪŋ] than [ɪn] will appear in Careful speech, as [ɪŋ] is the standard variant of (ING) and has indexical meanings including formality, effortfulness, articulateness and education (Campbell-Kibler 2007). Conversely, we expect that the nonstandard variant [ɪn], which has been found to index inarticulateness/unpretentiousness, relaxation, and uneducation (Campbell-Kibler 2007), will appear with a higher frequency than [ɪŋ] in Casual speech.
The Style Decision Tree
The mean (ing) rate for each stylistic category was used to evaluate the relative frequency between (ING) variants used in each category. Since the “0” code corresponds to the nonstandard apical [ɪn] variant and the “1” code to the standard velar [ɪŋ] variant, a mean (ING) rate closer to “1” indicates the standard variant was used more often. Conversely, a mean (ING) rate closer to “0” indicates increased usage of the nonstandard variant.
Mean (ing) rate by stylistic category
This table shows the mean (ing) rates per style category. A mean (ing) rate closer to 0 indicates greater usage of the nonstandard variant, whereas a mean (ing) rate closer to 1 indicates greater usage of the standard variant. The table must be interpreted carefully, however, because all of (ING)’s grammatical categories are lumped into the discrete style categories of the Style Decision Tree. These aggregate mean (ing) rates therefore may not accurately reflect stylistic differentiation that occurred within grammatical subsets of (ING). This is shown by the fact that the mean (ing) rates do not cluster into the “Casual” and “Careful” grammatical categories. Strikingly, the “Kids” and “Tangent” categories, both “Casual” according to the Style Decision Tree, have the highest mean (ing) rates, whereas the “Careful” “Language” and “Soapbox” categories both have lower mean (ing) rates.
## style Bin_style ing.rate
## 1: R Careful 0.4497817
## 2: L Careful 0.2631579
## 3: S Careful 0.3506098
## 4: C Careful 0.4241908
## 5: N Casual 0.2828125
## 6: G Casual 0.3828125
## 7: K Casual 0.4782609
## 8: T Casual 0.4711656Regression Analyses
We fit a logistic regression model to predict (ING) variant used from birth year, preceding segment, following segment, speaker gender, style, grammatical class, level of educational attainment and lexical frequency. A logistic regression was run after a generalized linear mixed effects model failed to converge. We tested for birth year/education and speaker gender/style interactions. Every predictor except style yields significant main effects, and a significant birth year/education interaction appears. However, as with the values in the mean (ING) rate table, the results of this regression must be interpreted with caution because the regression was run without separating the nominal, verbal and quantifier grammatical categories.
# Working in dplyr
library(dplyr)
# Working in lme4 (regressions)
library(lme4)
# Accessing data file
ing.lsa3 <- read.csv("ing.lsa3.csv")
# Turning ing.lsa3 into dataframe
ing.lsa3 <- data.frame(ing.lsa3)
# Making model for ALL grammatical categories: birthyear * education & gender * style
mod.ing.lsa.all.fin <- glm(code ~ birthyear * school.cat + Pre_Seg + Post_Seg + gender *
Bin_style + logfreq + newgram2, ing.lsa3, family = "binomial")
# Seeing the results of mod.ing.lsa.all.fin
summary(mod.ing.lsa.all.fin)##
## Call:
## glm(formula = code ~ birthyear * school.cat + Pre_Seg + Post_Seg +
## gender * Bin_style + logfreq + newgram2, family = "binomial",
## data = ing.lsa3)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4155 -0.8756 -0.5646 1.0027 2.2715
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 26.339383 6.137636 4.291 1.78e-05
## birthyear -0.012062 0.003142 -3.839 0.000124
## school.catNot HS -45.296690 10.745597 -4.215 2.49e-05
## school.catSome college -21.738879 8.393810 -2.590 0.009601
## Pre_Segcoronal.obs -0.846507 0.186001 -4.551 5.34e-06
## Pre_Segother -1.428586 0.175955 -8.119 4.70e-16
## Pre_Segvelar.N -2.482373 0.547082 -4.537 5.69e-06
## Post_Segpause 0.573719 0.091797 6.250 4.11e-10
## Post_Segvelar.C 0.051659 0.167874 0.308 0.758290
## genderm -1.068120 0.104626 -10.209 < 2e-16
## Bin_styleCasual -0.201488 0.105392 -1.912 0.055903
## logfreq -0.145758 0.021402 -6.810 9.73e-12
## newgram2p -0.993734 0.088338 -11.249 < 2e-16
## newgram2s -0.996756 0.169223 -5.890 3.86e-09
## birthyear:school.catNot HS 0.023434 0.005553 4.220 2.44e-05
## birthyear:school.catSome college 0.011628 0.004297 2.706 0.006809
## genderm:Bin_styleCasual 0.157157 0.158067 0.994 0.320106
##
## (Intercept) ***
## birthyear ***
## school.catNot HS ***
## school.catSome college **
## Pre_Segcoronal.obs ***
## Pre_Segother ***
## Pre_Segvelar.N ***
## Post_Segpause ***
## Post_Segvelar.C
## genderm ***
## Bin_styleCasual .
## logfreq ***
## newgram2p ***
## newgram2s ***
## birthyear:school.catNot HS ***
## birthyear:school.catSome college **
## genderm:Bin_styleCasual
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 4852.6 on 3598 degrees of freedom
## Residual deviance: 4073.3 on 3582 degrees of freedom
## AIC: 4107.3
##
## Number of Fisher Scoring iterations: 4Following Tagliamonte (2004) and using the full model’s predictors, we fit separate models for each grammatical class. For nominal and verbal (ING), preceding segment, following segment, speaker gender, grammar and lexical frequency are significant, but birth year, education, style and both interactions were significant only for verbal (ING). For quantifier (ING), birth year, education, speaker gender and the birth year/education interaction are significant.
Nominal (ING)
# Working in lme4 (regression)
library(lme4)
# Accessing data file
ing.lsa.nom2 <- read.csv("ing.lsa.nom2.csv")
# Making model for NOMINAL grammatical category: birthyear * education & gender * style
mod.ing.lsa.nom.fin <- glm(code ~ birthyear * school.cat + Pre_Seg + Post_Seg + gender *
Bin_style + logfreq, ing.lsa.nom2, family = "binomial")
# Seeing the results of mod.ing.lsa.nom.fin
summary(mod.ing.lsa.nom.fin)##
## Call:
## glm(formula = code ~ birthyear * school.cat + Pre_Seg + Post_Seg +
## gender * Bin_style + logfreq, family = "binomial", data = ing.lsa.nom2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6184 -0.9859 0.4517 0.8923 2.1110
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.542805 10.717232 0.704 0.48156
## birthyear -0.001893 0.005500 -0.344 0.73078
## school.catNot HS 32.318495 21.293045 1.518 0.12907
## school.catSome college -4.472474 15.196012 -0.294 0.76851
## Pre_Segcoronal.obs -1.175805 0.302244 -3.890 0.00010 ***
## Pre_Segother -2.220630 0.276889 -8.020 1.06e-15 ***
## Pre_Segvelar.N -2.612982 0.978582 -2.670 0.00758 **
## Post_Segpause 1.094174 0.186918 5.854 4.81e-09 ***
## Post_Segvelar.C 0.189499 0.289946 0.654 0.51339
## genderm -0.765698 0.194180 -3.943 8.04e-05 ***
## Bin_styleCasual -0.052205 0.213699 -0.244 0.80700
## logfreq -0.209430 0.039078 -5.359 8.36e-08 ***
## birthyear:school.catNot HS -0.016880 0.011019 -1.532 0.12554
## birthyear:school.catSome college 0.002569 0.007780 0.330 0.74127
## genderm:Bin_styleCasual -0.023596 0.306470 -0.077 0.93863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1353.7 on 1010 degrees of freedom
## Residual deviance: 1113.3 on 996 degrees of freedom
## AIC: 1143.3
##
## Number of Fisher Scoring iterations: 4Verbal (ING)
# Working in lme4 (regression)
library(lme4)
# Accessing data file
ing.lsa.verb2 <- read.csv("ing.lsa.verb2.csv")
# Making model for VERBAL grammatical category: birthyear * education & gender * style
mod.ing.lsa.verb.fin <- glm(code ~ birthyear * school.cat + Pre_Seg + Post_Seg + gender *
Bin_style + logfreq, ing.lsa.verb2, family = "binomial")
# Seeing the results of mod.ing.lsa.verb.fin
summary(mod.ing.lsa.verb.fin)##
## Call:
## glm(formula = code ~ birthyear * school.cat + Pre_Seg + Post_Seg +
## gender * Bin_style + logfreq, family = "binomial", data = ing.lsa.verb2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8724 -0.8414 -0.5944 1.0082 2.3901
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 27.557492 8.565283 3.217 0.00129
## birthyear -0.013592 0.004385 -3.099 0.00194
## school.catNot HS -75.924728 14.229862 -5.336 9.52e-08
## school.catSome college -26.969586 11.351919 -2.376 0.01751
## Pre_Segcoronal.obs -0.127070 0.278666 -0.456 0.64839
## Pre_Segother -0.448774 0.271738 -1.651 0.09864
## Pre_Segvelar.N -1.839008 0.695907 -2.643 0.00823
## Post_Segpause 0.520602 0.122385 4.254 2.10e-05
## Post_Segvelar.C -0.197611 0.235732 -0.838 0.40187
## genderm -1.424837 0.143027 -9.962 < 2e-16
## Bin_styleCasual -0.417386 0.130647 -3.195 0.00140
## logfreq -0.142696 0.026946 -5.296 1.19e-07
## birthyear:school.catNot HS 0.039368 0.007348 5.357 8.44e-08
## birthyear:school.catSome college 0.014371 0.005813 2.472 0.01343
## genderm:Bin_styleCasual 0.487908 0.210607 2.317 0.02052
##
## (Intercept) **
## birthyear **
## school.catNot HS ***
## school.catSome college *
## Pre_Segcoronal.obs
## Pre_Segother .
## Pre_Segvelar.N **
## Post_Segpause ***
## Post_Segvelar.C
## genderm ***
## Bin_styleCasual **
## logfreq ***
## birthyear:school.catNot HS ***
## birthyear:school.catSome college *
## genderm:Bin_styleCasual *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2724.3 on 2163 degrees of freedom
## Residual deviance: 2389.7 on 2149 degrees of freedom
## AIC: 2419.7
##
## Number of Fisher Scoring iterations: 4Quantifier (ING)
# Working in lme4 (regression)
library(lme4)
# Accessing data file
ing.lsa.quant2 <- read.csv("ing.lsa.quant2.csv")
# Making model for QUANTIFIER grammatical category: birthyear * education & gender * style
mod.ing.lsa.quant.fin <- glm(code ~ birthyear * school.cat + Post_Seg + gender *
Bin_style + logfreq, ing.lsa.quant2, family = "binomial")
# Seeing the results of mod.ing.lsa.quant.fin
summary(mod.ing.lsa.quant.fin)##
## Call:
## glm(formula = code ~ birthyear * school.cat + Post_Seg + gender *
## Bin_style + logfreq, family = "binomial", data = ing.lsa.quant2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4242 -0.8515 -0.5147 0.9151 2.1255
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 128.05179 32.91105 3.891 9.99e-05
## birthyear -0.06795 0.01688 -4.026 5.67e-05
## school.catNot HS -156.71526 42.62324 -3.677 0.000236
## school.catSome college -123.23108 36.35154 -3.390 0.000699
## Post_Segpause -0.39439 0.27381 -1.440 0.149764
## Post_Segvelar.C 0.97548 0.54137 1.802 0.071567
## genderm -0.95224 0.32839 -2.900 0.003735
## Bin_styleCasual 0.31540 0.35704 0.883 0.377029
## logfreq 0.29160 0.40329 0.723 0.469640
## birthyear:school.catNot HS 0.08095 0.02200 3.679 0.000234
## birthyear:school.catSome college 0.06418 0.01869 3.433 0.000596
## genderm:Bin_styleCasual -0.68453 0.50366 -1.359 0.174111
##
## (Intercept) ***
## birthyear ***
## school.catNot HS ***
## school.catSome college ***
## Post_Segpause
## Post_Segvelar.C .
## genderm **
## Bin_styleCasual
## logfreq
## birthyear:school.catNot HS ***
## birthyear:school.catSome college ***
## genderm:Bin_styleCasual
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 530.56 on 423 degrees of freedom
## Residual deviance: 430.85 on 412 degrees of freedom
## AIC: 454.85
##
## Number of Fisher Scoring iterations: 6The speaker gender/style interaction within verbal (ING) revealed that women style-shift more dramatically than men do. *clarify what “shifting more dramatically” means
## working on fixing this!!
# Working in dplyr
library(dplyr)
# Working in ggplot (graphing)
library(ggplot2)
# Accessing data file
ing.lsa.verb2 <- read.csv("ing.lsa.verb2.csv")
#turn into dplyr dataframe
ing.lsa.verb3 <- tbl_df(ing.lsa.verb2)
#say what dataframe
ing.lsa.verb3 <- ing.lsa.verb3 %>%
#will create data in long format
group_by(Bin_style, newgram2, gender, school.cat, birthyear) %>%
#summarize; add the ret rate and N column
summarise(index=mean(code),N=n())
# Making the graph - for only VERBAL ing
ggplot(ing.lsa.verb3, aes(gender,index)) +
geom_bar(stat = "identity", aes(fill=Bin_style), position = "dodge") +
ggtitle("Style-shifting by gender for verbal (ING)") +
labs(x = "Gender", y = "Rate of velar (ING)",
title = "Style-shifting by gender for verbal (ING)",
fill = "Style")
Discussion
While suprising, these findings resemble the grammatically differentiated constraint ranking for (ING) observed by Tagliamonte in a 2004 study of York English. Of the internal and external factors that Tagliamonte evaluated, grammatical category most significantly influenced patterns of (ING) usage (Tagliamonte 2004:398). Subsequent analysis of constraint ranking for nominal compared to verbal (ING) revealed that “NOUNS and VERBS have entirely separate and unique linguistic and social profiles” (Tagliamonte 2004:399). Although differences exist in the factors that Tagliamonte and I found to be significant, in both studies, of two distinct varieties of English, grammatical category primarily conditioned variation within (ING). The significant role of grammar in mediating variation within (ING) is not new (see Houston 1985; Marsh 1866), but as Tagliamonte emphasizes sufficient attention has not be dedicated to examining this trend.
It is striking that stylistic constraints primarily distinguish each grammatical category; these categories have not consistently been differentiated in literature on (ING)’s indexical meaning (Eckert 2012, Podesva 2007, Kiesling 2008). This evidence supports the claim that (ING) is more than one variable (Tagliamonte 2004:400, Tamminga 2014:5). Verbal and quantifier (ING) demonstrated birth year/education interactions, shown by decreasing /ing/ rates for speakers who only attended high school. This reflects college education’s changing prevalence, as finishing high school is no longer a definitive social achievement. Birth year’s significant main effect, in addition to its interaction with education (as in Horvath 1985 and Labov 1972), counter (ING)’s corroborated stability (summarized Labov 2001a:86), which suggests a change in progress.
# Working in dplyr
library(dplyr)
# Working in ggplot (graphing)
library(ggplot2)
# Accessing data file
ing.lsa2 <- read.csv("ing.lsa2.csv")
# Turning ing.lsa2 into dataframe
ing.lsa2 <- data.frame(ing.lsa2)
# Reordering educational attainment values
ing.lsa2$school.cat <- factor(ing.lsa2$school.cat, c("Not HS","Just HS","Some college"))
# Creating graph
ggplot(ing.lsa2, aes(birthyear, code, color = school.cat)) +
geom_point(aes(color=school.cat)) +
stat_smooth(aes(color=school.cat, fill=school.cat), method = "lm") +
labs(x = "Birth year", y = "Rate of velar (ING) (%)",
color = "Educational attainment", fill="Educational attainment") +
ggtitle("/ing/ rate by education over time")
That the rate of (ING) use is changing over time with educational achievement is striking, and reinforces how deeply patterns of language usage are entrenched in greater social phenomena. This is interesting given (ING)’s purported stability (summarized Labov 2001a:86), but more significantly because it implies further investigations of (ING) can be used to probe socially constructed realities that are evolving below the level of consciousness. Eckert (2012:94) identifies “indexical mutability” as the core property of linguistic variables in the third wave study of variation. That (ING)’s usage was shown to change over time suggests it is not immune to the processes of bricolage by which speakers reconstruct variables to reflect their community’s changing social concerns.
Conclusion
Grammatically differentiated conditioning within (ING) draws attention to what the study of (ING) has yet to teach us, emphasizing the variable’s continued relevance in third wave studies of variation. It appears subsequent studies of (ING) have the potential to deepen our understanding of indexicality and processes of speaker agency such as stance (Kiesling 2008). At a moment when understanding “the dynamics of variation in individuals” (Tamminga, MacKenzie & Embick 2015:2) is of emergent scientific interest, (ING) presents a unique opportunity to investigate variation at the individual level, in many individuals simultaneously. Analysis of larger, more variegated corpora, from diverse contexts, may be necessary to further understand (ING)’s socio-indexical meanings.
References
Campbell-Kibler, K. (2007). Accent, (ING), and the social logic of listener perceptions. American Speech, 82, 1, pp. 32-64. ● Eckert, P. (2012). Three waves of variation study: the emergence of meaning in the study of sociolinguistic variation. In Annual Review of Anthropology, 41, 87-100. ● Fischer, J. (1958). Social influences on the choice of a linguistic variant. In Word, 14, 1, pp. 47-56. ● Horvath, B.M. (1985). Variation in Australian English. Cambridge: Cambridge University Press. ● Houston, A. (1985). Continuity and change in English morphology: the variable (ING). PhD thesis, University of Pennsylvania. ● Kiesling, S.F. (2008). Style as stance: Stance as the explanation for patterns of sociolinguistic variation. In Alexandra Jaffe (ed), Sociolinguistic Perspectives on Stance. Oxford University Press. pp. 171-194. ● Labov, W. (1966/1982). The Social Stratification of English in New York City. Washington, D.C.: Center for Applied Linguistics. ● Labov, W. (1972). Sociolinguistic patterns. Philadelphia: University of Pennsylvania Press. ● Labov, W. (2001a). Principles of Linguistic Change: Volume 2, Social Factors. Blackwell Publishers. ● Labov, W. (2001b). The anatomy of style-shifting. In Eckert & Rickford (Eds.), Style and Sociolinguistic Variation. Cambridge University Press. ● Labov, W. & I. Rosenfelder. (2011). The Philadelphia Neighborhood Corpus. ● Podesva, R. (2007). Three sources of stylistic meaning. Texas Linguistic Forum 51. ● Tagliamonte, S.A. (2004). Someth[in]’s go[ing] on!: Variable ing at ground zero. In Gunnarsson et al. (eds.), Language Variation in Europe: papers from the Second International Conference on Language Variation in Europe, ICLA VE 2 Uppsala, Sweden, June 12-14, 2003. ● Tamminga, M. (2014). Persistence in the Production of Linguistic Variation. Ph.D. Dissertation, University of Pennsylvania. ● Tamminga, M., MacKenzie, L. & D. Embick. (forthcoming). The dynamics of variation in individuals. Paper under review at Language Variation. ● Trudgill, P. (1974). The social differentiation of English in Norwich. Cambridge: University of Cambridge Press.