The West low homogeneity and low consistency (Labov, Ash, Boberg - - PowerPoint PPT Presentation

THE LINGUISTIC EFFECTS OF A CHANGING TIMBER INDUSTRY

LANGUAGE CHANGE IN COWLITZ COUNTY, WA

The 4th Annual Linguistics Conference at UGA October 6, 2017 Athens, Georgia

Joseph A. Stanley

University of Georgia @joey_stan joeystanley.com

COWLITZ COUNTY, WASHINGTON

2

Introduction

Sparsely populated before 1920s Longview founded in 1923

• R. A. Long

Two large lumber mills Population ≈ 35,000 Right off I-5 Two hours south of Seattle One hour north of Portland

The West

“low homogeneity” and “low consistency”

(Labov, Ash, Boberg 2006:277)

cot-caught merger fronting of /u/ lack of Southern, Midland, and Canadian features

3

Background

PACIFIC NORTHWEST ENGLISH

4

Background

• æg

ɛg eg

(Wassink et al. 2009, Freeman 2014, Riebold 2015, Wassink 2015, Wassink 2016, etc.)

(Ward 2003, Becker et al. 2013, McLarty & Kendall 2014, Becker et al. 2016, etc.)

(dragon, snag, agony, brag, wagon, jaguar) (peg, legacy, integrity, segment) (vague, plague, flagrant) (low, sew, row, throw, mow, show, go, toe, know, doe)

Linguistic changes happened because of the changing timber industry.

HYPOTHESIS

5

Background

METHODOLOGY

41 natives of Cowlitz County, ages 18–70s 29-item word list (see appendix slides) forced aligned with DARLA (Reddy & Stanford 2015), which uses ProsodyLab (Gorman et al. 2011) and FAVE (Rosenfelder et al. 2014) A Praat script extracted formants at 15 points along vowel trajectories. Bark normalized measurements (Traunmüller 1997)

Lobanov transformation not used because I’m not working with the full vowel space (Thomas & Kendall 2015)

DATA COLLECTION

7

Methodology

Num Number ber o

• f t

tokens ens pre-velars 549 /o/ 348 total 897

Mixed-effects models (Baayen 2008)

lmer() in the R package lme4 (Bates et al. 2015)

Searched for the best breakpoint. Appendix slides:

more detailed explanation of statistical methods all model outputs interpretation of each model

ANALYSIS

8

Methodology

150 200 250 40 60 80

age F1

150 200 250 40 60 80

age F1

150 175 200 225 250 275 40 60 80

age F1

150 175 200 225 250 275 20 40 60 80

age F1

LANGUAGE CHANGE: PRE-VELARS

PRE-VELARS: DISTRIBUTION

10

Results: Pre-Velars

bacon exit deck black exit deck black deck exit black bacon exit black bacon deck bacon deck exit black exit black bacon deck deck black bacon exit deck exit bacon black deck exit black bacon deck bacon black exit exit black bacon deck bacon black exit deck deck black bacon exit exit deck black bacon deck bacon exit black bacon black deck exit deck exit bacon black exit bacon black deck deck bacon exit black black deck bacon exit bacon black exit deck bacon black exit black deck exit bacon bacon deck black exit exit bacon black deck black bacon deck exit black exit bacon deck exit black bacon deck exit bacon deck black exit black deck bacon deck bacon exit black deck black bacon exit flagrant snag regular agony vague legacy jaguar rag peg dragon plague integrity segment brag wagon rag jaguar integrity plague vague segment wagon legacy flagrant snag vague plague jaguar integrity regular brag dragon agony peg segment rag flagrant wagon segment vague agony snag dragon peg rag legacy jaguar integrity plague brag regular jaguar dragon peg integrity regular legacy brag wagon flagrant rag plague vague snag segment agony brag peg snag regular vague integrity plague wagon legacy agony rag jaguar dragon segment flagrant jaguar regular rag plague brag integrity snag vague dragon agony flagrant peg legacy wagon segment wagon flagrant agony segment dragon jaguar brag legacy peg integrity brag regular snag plague vague peg brag plague dragon jaguar vague regular agony snag legacy wagon flagrant rag segment integrity snag agony flagrant regular brag dragon segment plague integrity vague wagon peg legacy jaguar rag plague peg regular rag wagon dragon jaguar agony brag legacy snag integrity segment vague vague flagrant dragon agony wagon snag regular brag plague legacy jaguar integrity peg vague rag dragon segment snag peg jaguar wagon agony vague integrity flagrant regular legacy brag rag plague snag wagon plague agony brag flagrant segment vague peg jaguar integrity rag regular dragon legacy rag vague plague jaguar brag integrity snag segment legacy wagon regular flagrant agony peg dragon brag wagon jaguar snag flagrant agony dragon rag plague regular segment peg vague legacy integrity dragon agony plague snag wagon legacy segment regular vague brag integrity rag jaguar peg agony rag wagon legacy snag peg jaguar plague integrity segment dragon regular vague brag flagrant segment rag vague dragon brag regular agony integrity legacy flagrant snag jaguar plague wagon peg integrity rag wagon peg legacy segment regular plague brag snag agony dragon vague flagrant jaguar segment snag vague jaguar integrity flagrant plague peg legacy dragon rag agony brag regular wagon agony integrity wagon regular brag peg snag segment vague rag jaguar legacy plague dragon regular segment agony dragon wagon jaguar plague vague peg rag legacy brag flagrant snag integrity dragon wagon flagrant legacy segment vague jaguar plague peg rag regular brag integrity snag agony agony plague brag vague snag integrity dragon flagrant rag regular jaguar segment peg wagon legacy legacy flagrant peg segment rag plague brag jaguar wagon integrity dragon snag regular vague agony jaguar snag wagon dragon peg regular segment plague brag rag integrity flagrant vague legacy agony dragon plague vague peg segment agony integrity legacy rag integrity wagon brag regular snag jaguar agony wagon plague vague rag regular jaguar legacy integrity snag segment peg flagrant rag dragon jaguar agony vague segment snag legacy brag flagrant integrity peg dragon rag regular wagon plague dragon wagon integrity segment vague snag jaguar peg plague legacy regular brag agony rag segment jaguar integrity plague agony peg wagon snag flagrant legacy dragon brag vague regular rag

k g 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 4 6 8 10

backness height variable

a a a

vague beg bag

Pre-velar tokens by all speakers

BAG is raised to the

/ɛk/ space

SEX + GENERATION

11

Results: Pre-Velars

high overlap between VAGUE and BEG for all groups

• lder men raise BAG almost to

merge with VAGUE/BEG young people didn’t raise BAG

flagrant snag regular agony vague legacy jaguar rag peg dragon plague integrity segment brag wagon brag peg snag regular vague integrity plague wagon legacy agony rag jaguar dragon segment flagrant snag agony flagrant regular brag dragon segment plague integrity vague wagon peg legacy jaguar rag dragon agony wagon snag regular brag plague legacy jaguar integrity peg vague rag dragon segment snag peg jaguar wagon agony vague integrity flagrant regular legacy brag rag plague snag wagon plague agony brag flagrant segment vague peg jaguar integrity rag regular dragon legacy agony rag wagon legacy snag peg jaguar plague integrity segment dragon regular vague brag flagrant integrity rag wagon peg legacy segment regular plague brag snag agony dragon vague flagrant jaguar segment snag vague jaguar integrity flagrant plague peg legacy dragon rag agony brag regular wagon regular segment agony dragon wagon jaguar plague vague peg rag legacy brag flagrant snag integrity agony plague brag vague snag integrity dragon flagrant rag regular jaguar segment peg wagon legacy legacy flagrant peg segment rag plague brag jaguar wagon integrity dragon snag regular vague agony jaguar snag wagon dragon peg regular segment plague brag rag integrity flagrant vague legacy agony flagrant wagon segment vague agony snag dragon peg rag legacy jaguar integrity plague brag regular jaguar dragon peg integrity regular legacy brag wagon flagrant rag plague vague snag segment agony jaguar regular rag plague brag integrity snag vague dragon agony flagrant peg legacy wagon segment peg brag plague dragon jaguar vague regular agony snag legacy wagon flagrant rag segment integrity plague peg regular rag wagon dragon jaguar agony brag legacy snag integrity segment vague vague flagrant segment rag vague dragon brag regular agony integrity legacy flagrant snag jaguar plague wagon peg dragon wagon integrity segment vague snag jaguar peg plague legacy regular brag agony rag rag jaguar integrity plague vague segment brag wagon jaguar snag flagrant agony dragon rag plague regular segment peg vague legacy integrity dragon agony plague snag wagon legacy segment regular vague brag integrity rag jaguar peg agony integrity wagon regular brag peg snag segment vague rag jaguar legacy plague dragon dragon wagon flagrant legacy segment vague jaguar plague peg rag regular brag integrity snag agony dragon plague vague peg segment agony integrity legacy rag integrity wagon brag regular snag jaguar jaguar agony vague segment snag legacy brag flagrant integrity peg dragon rag regular wagon plague segment jaguar integrity plague agony peg wagon snag flagrant legacy dragon brag vague regular rag wagon legacy flagrant snag vague plague jaguar integrity regular brag dragon agony peg segment rag wagon flagrant agony segment dragon jaguar brag legacy peg integrity brag regular snag plague vague rag vague plague jaguar brag integrity snag segment legacy wagon regular flagrant agony peg dragon agony wagon plague vague rag regular jaguar legacy integrity snag segment peg flagrant rag dragon

M

• lder

M younger F

• lder

F younger 2 4 6 8 2 4 6 8 6 8 10 6 8 10

backness height variable

a a a

vague beg bag

Pre-lateral tokens by sex and generation

6 8 10 1942 1951 1956 1959 1960 1961 1962 1966 1967 1968 1970 1976 1981 1984 1985 1987 1990 1991 1992 1996 1997

year of birth height Height of /æg/ by year of birth

Regression Model

(see model 1 in the appendix)

Best generation split was around 1970 (46 years old)

12

Results: Pre-Velars

LANGUAGE CHANGE: /O/

/O/ FRONTING

/o/ is gradually fronting over time (see model 2 in the appendix) marginally significant break- point at 1970 (Baayan 2008 §6.4)

14

Results: Back Vowels

1942 1951 1956 1959 1960 1961 1962 1966 1967 1968 1970 1976 1981 1984 1985 1987 1990 1991 1992 1996 1997 2 4 6 8

backness year of birth Backness of /o/ by year of birth

distance from 20% to 80% messy data still, but the numbers match my intuition

TRAJECTORIES

15

Results: Back Vowels

10-Daniel 29-Amanda 1000 1500 2000 1000 1500 2000 200 400 600

F2 F1 word

doe go know low mow row sew show toe

Diphthongization of /o/ over time

younger generation = more diphthongal

(see model 3 in the appendix)

jump at 1970

16

250 500 750 1000 1942 1951 1956 1959 1960 1961 1962 1966 1967 1968 1970 1976 1981 1984 1985 1987 1990 1991 1992 1996 1997

year of birth dist Distance by year of birth

Older (born before 1970) Younger (born after 1970)

BAG

raised lowered /o/ quality back fronted /o/ trajectory monophthongal diphthongal

LINGUISTIC SUMMARY

WHAT HAPPENED IN 1970??

It– it really affected the woods becau– there were a lot of people that worked in the

• woods. And if they didn't work in the woods they– they were like support system, like
• ffice people. So if they're not working out in the woods then all these office people—

even as far as Tacoma where the headquarters were—were getting laid off because these guys couldn't get in. And I totally understand but it– A lot of people lost their jobs and a lot of people

• moved. A lot of people just got out of here. And so you take that kind of income from

these people out in the woods—and they made really good money considering, y'know—okay what does that do to the rest of your economy? They're no longer buying as much gas. They're not– They can't afford to go out and go to the movies, and eat out, and groceries, and yeah. So yeah, it hit us especially hard.

“CAROL”

19

2500 5000 7500 10000 1970 1980 1990 2000 2010

Year Jobs Cowlitz Timber Employment

CHANGES IN THE TIMBER INDUSTRY

20

16000 18000 20000 22000 1970 1980 1990 2000 2010

year per_capita_earnings Inflation-Adjusted Earnings Per Capita

CHANGES IN THE TIMBER INDUSTRY

21

CHANGES IN THE TIMBER INDUSTRY

22

• 1000

1000 1960 1980 2000 2020

year in migration Cowlitz In-Migration

CHANGES IN THE TIMBER INDUSTRY

23

0.00 0.05 0.10 0.15 0.20 <5 5-9 10-14 15-19 20-29 30-44 45-59 >60

minutes proportion year

1980 1990 2000 2008

Commute Time

Major changes in late 1970s–1980s Fewer logging jobs. Less income. Less insularity. More contact with Portland and the rest of the Pacific Northwest. Local? Regional? National? Not sure.

CENSUS SUMMARY

24

CONCLUSION

Older (born before 1970) Younger (born after 1970)

BAG

raised lowered /o/ quality back fronted /o/ trajectory monophthongal diphthongal jobs loggers diverse economy booming recession network insular expanded

SUMMARY

✓ Linguistic changes happened because of the changing timber industry.

CONCLUSION

27

Conclusion

Baayen, R. H. 2008. Analyzing Linguistic Data: A Practical Introduction to Statistics using R. Cambridge: Cambridge University Press. Bates, Douglas, Martin Maechler, Ben Bolker & Steve Walker. 2015. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software 67(1). 1–48. doi:doi:10.18637/jss.v067.i01. Bauer, Matt & Frank Parker. 2008. /æ/-raising in Wisconsin English. American Speech 83(4). 403–431. Becker, Kara, Anna Aden, Katelyn Best, Rena Dimes, Juan Flores & Haley Jacobson. 2013. Keep Portland weird: Vowels in Oregon English. Paper presented at the New Ways of Analyzing Variation (NWAV) 42, Pittsburgh. Becker, Kara, Anna Aden, Katelyn Best & Haley Jacobson. 2016. Variation in West Coast English: The case of Oregon. In Valerie Fridland, Betsy E. Evans, Tyler Kendall & Alicia Beckford Wassink (eds.), Speech in the Western States, Vol. 1: The Pacific Coast, 107–134. (Publication of the American Dialect Society 101). Durham, NC: Duke University Press. doi: 10.1215/00031283-3772923. Cardoso, Amanda. Pre-velar Raising in Western Canadian Dialects. Unpublished manuscript. Cited in Cardoso et al. (2016). Cardoso, Amanda, Lauren Hall-Lew, Yova Kementchedjhieva & Ruaridh Purse. 2016. Between California and the Pacific Northwest: The front lax vowels in San Francisco English. In Betsy Evans, Valerie Fridland, Tyler Kendall & Alicia Wassink (eds.), Speech in the Western States: Volume 1: The Coastal States, 33–54. (Publication of the American Dialect Society 101). Durham, NC: Duke University Press. doi: 10.1215/00031283-3772890. Freeman, Valerie. 2014. Bag, beg, bagel: Prevelar raising and merger in Pacific Northwest English. University of Washington Working Papers in Linguistics 32. Fridland, Valerie, Tyler Kendall & Craig Fickle. 2015. It’s Neva-ae-da, not nev-ah-da. Paper. Paper presented at the Annual Meeting of the American Dialect Society, Portland, Oregon. Labov, William, Sharon Ash & Charles Boberg. 2006. The Atlas of North American English: Phonetics, Phonology and Sound Change. Walter de Gruyter. McLarty, Jason & Tyler Kendall. 2014. The relationship between the high and mid back vowels in Oregonian English. Paper presented at the New Ways of Analyzing Variation (NWAV) 43, Chicago. Gorman, Kyle, Jonathan Howell & Michael Wagner. 2011. Prosodylab-Aligner: A Tool for Forced Alignment of Laboratory Speech. Canadian Acoustics 39(3). 192–193. Levshina, Natalia. 2015. How to do Linguistics with R: Data exploration and statistical analysis. Amsterdam: John Benjamins Publishing Company. McLarty, Jason, Tyler Kendall & Charlie Farrington. 2016. Investigating the development of the contemporary Oregonian English vowel system. In Valerie Fridland, Betsy E. Evans, Tyler Kendall & Alicia Beckford Wassink (eds.), Speech in the Western States, Vol. 1: The Pacific Coast, 135–157. (Publication of the American Dialect Society 101). Durham, NC: The American Dialect Society. doi: 10.1215/00031283-3772934. Reddy, Sravana & James N. Stanford. 2015. Toward completely automated vowel extraction: Introducing DARLA. Linguistics Vanguard. doi:10.1515/lingvan-2015-0002 (26 October, 2015). Riebold, John Matthew. 2015. The Social distribution of a regional change: /æg, ɛg, eg/ in Washington

• State. Seattle: University of Washington PhD dissertation.

Roeder, Rebecca. 2009. The effects of phonetic environment on English /ae/among speakers of Mexican heritage in Michigan. Toronto Working Papers in Linguistics 31. http://twpl.library.utoronto.ca/index.php/twpl/article/view/6090 (6 April, 2017). Rosenfelder, Ingrid, Josef Fruehwald, Keelan Evanini, Scott Seyfarth, Kyle Gorman, Hilary Prichard & Jiahong Yuan. 2014. FAVE (Forced Alignment and Vowel Extraction) Program Suite v1.2.2. Thomas, Erik, and Tyler Kendall. "NORM: Vowel Normalization Suite 1.1 | Methods." N.p., 18 Nov.

• 2015. Web. Accessed 12 January, 2017.

Traunmüller, Hartmut. 1997. Auditory scales of frequency representation. Stockholms universitet: Instituionen för lingvistik. http://www2.ling.su.se/staff/hartmut/bark.htm (17 November, 2016). Ward, Michael. 2003. Portland dialect study: The fronting of/ ow, u, uw/ in Portland, Oregon. Portland State University Master’s Thesis. http://www.pds.pdx.edu/Publications/Ward.pdf (15 February, 2016). Wassink, Alicia Beckford. 2015. Sociolinguistic Patterns in Seattle English. Language Variation and Change 27(1). 31–58. doi:10.1017/S0954394514000234. Wassink, Alicia Beckford. 2016. The Vowels of Washington State. In Betsy Evans, Valerie Fridland, Tyler Kendall & Alicia Wassink (eds.), Speech in the Western States: Volume 1: The Coastal States, 77–

• 105. (Publication of the American Dialect Society 101). Durham, NC: Duke University Press.

10.1215/00031283-3772912. Wassink, Alicia Beckford, Robert Squizzero, Mike Scanlon, Rachel Schirra & Jeff Conn. 2009. Effects of Style and Gender on Fronting and Raising of /æ/, /e:/ and /ε/ before /ɡ/ in Seattle English. Paper presented at the New Ways of Analyzing Variation (NWAV) 38, Ottawa.

REFERENCES

28

Conclusion

Special thanks to Cathy Jones for invaluable help in finding research participants, to the University of Georgia Graduate School Dean’s Award for funding the fieldwork, and to Scott Bailey for the census data. These slides available at joeystanley.com/lcuga4

29

Joey Stanley

University of Georgia joeystan@uga.edu @joey_stan joeystanley.com

APPENDIX A: WORD LIST AND MINIMAL PAIRS

WORD LIST ITEMS

31

Appendices

/eg/ flagrant, plague, vague (bacon) /ɛg/ exit, integrity, legacy, peg, regular, segment (deck) /æg/ agony, brag, dragon, jaguar, rag, snag, wagon (black) /o/ bow, doe, go, know, low, mow, row, sew, show, toe These were embedded psuedorandomly in a 160- item word list, with words targeting other research questions acting as fillers. Participants often commented on how random the words seemed, so they likely did not catch on to the research questions these words targeted. Words in parintheses were used as pre-voiceless reference points.

APPENDIX B: STATISTICAL TESTS

I use generalized linear mixed-effects models (Baayen

2008) using the function glmer() in the R package

lme4 (Bates et al. 2015), with speaker and word as

random effects and sex and some form of age/generation as a fixed effect. The older generation was defined as those born

• n or before 1970.

Effects are reported significant if p<0.01. For each hypothesis, three models were tested to see how age should be coded that included either 1) age as a continuous factor, 2) generation as a binary variable, or 3) only the interaction of age and generation to test the breakpoint. All three models fit using maximum liklihood (ML) and were compared to a model without age at all (a null model) using the anova() function. The model with the lowest BIC was chosen and refit using restricted maximum liklihood (REML). The

• utput of these final models is given in the

following slides. See Baayan (2008) for regression with breakpoints, and Levshina (2015) for model comparison.

ANALYSIS

33

Methodology

34

Appendices Fixed effects Value Std.Error t-value (Intercept) 7.886 0.212 37.16 sex: M 0.599 0.281 2.13 generation: younger –1.455 0.278 –5.24 Interpretation: The younger generation produced a lower BAG vowel than the older generation. The effect of sex was only marginally significant based on the small t-value (<3). Random effects Variance

• Std. Dev.

word 0.484 0.696 speaker 0.048 0.219 residual 0.598 0.773

(1) Linear mixed-effects model fit by REML of bark-normalized height (bark(F3)–bark(F1)) of pre-velar vowels with sex (F*, M) and generation (older, younger) as fixed effects and speaker and word as random effects. * Underlined values are the reference levels (2) Linear mixed-effects model fit by REML of bark-normalized backness (bark(F3)–bark(F2)) of /o/ with sex (F*, M) and age (as a continuous variable) as fixed effects and speaker and word as random effects.

Fixed effects Value Std.Error t-value (Intercept) 4.312 0.337 12.78 sex: M 0.326 0.215 1.52 generation: younger –0.034 0.007 –5.11 Interpretation: The model technically shows that the older someone was the backer their /o/ vowel would be. To put it another way, /o/ is fronting in apparent time. The effect of sex was not significant based on the small t-value (<2). Random effects Variance

• Std. Dev.

word 0.274 0.523 speaker 0.038 0.195 residual 0.662 0.813

35

Appendices Fixed effects Value Std.Error t-value (Intercept) 387.92 34.72 11.17 sex: M –110.88 27.95 –3.967 generation: younger 96.59 27.56 3.504 Interpretation: The younger generation had longer trajectories than the

• lder generation. Men had shorter trajectories than women.

Random effects Variance

• Std. Dev.

word 4767 69.04 speaker 9274 96.30 residual 10082 100.41

(3) Linear mixed-effects model fit by REML of trajectories of /o/ with sex (F*, M) and generation (older, younger) as fixed effects and speaker and word as random effects. * Underlined values are the reference levels