Identity and place: The changing role of Swabian in modern Germany - - PowerPoint PPT Presentation
Identity and place: The changing role of Swabian in modern Germany Karen V. Beaman Queen Mary, University of London Eberhard Karls University of Tbingen Conference on Language, Place and Periphery University of Copenhagen, Denmark January
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Karen V. Beaman Queen Mary, University of London Eberhard Karls University of Tübingen Conference on Language, Place and Periphery University of Copenhagen, Denmark January 18-19, 2018
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The goals of this research are:
dialect features.
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Auer (2005); Britain (2009); Dorian (1978); Schilling-Estes & Wolfram (1999); Moore & Carter (2015); Smith & Durham (2012); Trudgill (1983, 1986); Vandekerckhove & Britain (2009); Wagener (2002)
Labov (1966, 1975, 1991, 2001, ...); Sankoff (2005, 2006); Sankoff & Blondeau (2007); Wagner & Sankoff (2011)
Buchstaller (2015, 2016); Chambers (2003); Denis & Tagliamonte (2017); Rickford & Price (2013); Sankoff & Laberge (1978); Tagliamonte & D’Arcy (2009); Wagner (2012)
Auer (2007); Blommaert (2016); Britain (2012, 2013, 2016); Coupland (2016); Johnstone et al. (2006); Johnstone (2011); Milroy (2002)
Cheshire (2005); Coupland (2001); Eckert (1988); Johnstone (2016); Hoffmann & Walker (2010); LePage & Tabouret-Keller (1985); Sharma & Rampton (2015)
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Swabian or Schwäbisch is a High German dialect, belonging to the Alemannic family, spoken by just over 800,000 people. Two communities:
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Panel Study
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Level Description Variation (SWG ~ STD) Examples (SWG ~ STD) Phonological 1 STP – /-st/ Palatalisation
[∫t] ~ [st]
[fɛ∫t] ~ [fɛst] Fest ‘party’ 2 AIS1 – /ai/ Diphthong Shift
[ɔi] ~ [ai]
[gləin] / [glɔin] ~ [klain] klein ‘small’ 3 ANN – /an/ Nasalisation
[ã] ~ [an]
[mã kã] ~ [man kan] man kann ‘one can' 4 FRV1 – /ö/ Vowel Shift
[e] ~ [ö]
[meglɪç] ~ [møːklɪç] möglich ‘possible’ 5 LEO – Long /e/ Opening
[ä] ~ [e]
[i læsə] ~ [ɪç leːzə] ich lese ‘I read’ Morpho-syntactic 6 EDP – Plural Inflection
[ed] ~ [en]
[mɪr ma:xəd] ~ [vɪr ma:xən] wir machen ‘we do/make’ 7 IRV1 – Irregular Verb ‘gehen’
[gangəd] ~ [ge:ən]
[mɪr gangəd] ~ [vɪr ge:ən] wir gehen 'we go' 8 IRV3 – Irregular Verb ‘haben’
[hen] ~ [habən]
[mɪr hen] ~ [vɪr habən] wir haben 'we have' 9 SAF1 – Diminutive Affix ‘le’
bissle ~ bisschen 'little‘ 10 PVB – Periphrastic Verb ‘tun’
däd ~ würde
es däd beeinflusse ~ es würde beeinflussen ‘it should influence'
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Fixed Effects:
Random Effects: Interviewer Name – five interviewers Speaker ID – 13 speakers, 26 interviews
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e Mã, dr koi Spätzle mache kã, is koi richtige Mã. ein Mann, der kein Spätzle machen kann, ist kein richtiger Mann. a man, who can’t make Spätzle, is not a real man.
Swabian Allegiance:
1-1. Self-Declared Swabian: Are you a ‘real’ Swabian?
5=definitely, 4=maybe, 3=don't know, 2=not really, 1=no
1-2. Non-Swabian Friends: Do you have friends who are NOT Swabian?
5=no, 4=a few, 3=don't know, 2=many, 1=a lot
1-3. Swabian Ridicule: Do they laugh at how you speak?
5=always, 4=sometimes, 3=don't know, 2=not really, 1=not at all
1-4. Accommodation: Do you change how you speak?
5=not at all, 4=a little, 3=don't know, 2=a lot, 1=always
Swabian Language Attitudes:
2-1. Opinion of Swabian Language: What do you think of the Swabian language?
5=super, 4=good, 3=don’t know, 2=not good, 1=awful
2-2. Job Prospects for Swabians: Is it difficult to find a job when you speak Swabian?
5=great, 4=good, 3=no impact/don’t know, 2=maybe some, 1=very difficult
2-3. Swabians Speaking German: Is it odd when a Swabian speaks standard German?
5=very odd/awful, 4=funny, 3=don’t know, 2=good, 1=great
2-4. Non-Swabians Speaking Swabian: Is it odd when a non-Swabian speaks Swabian?
5=very odd/awful, 4=funny, 3=don’t know, 2=good, 1=great
Swabian Cultural Competence:
3-1. Swabian Knowledge: Are there different Swabian dialects?
5=considerable, 4=some, 3=don’t know, 2=not much, 1=none
3-2. Swabian Specialties: Do you know how to make Spätzle? Maultaschen?
5=of course, 4=somewhat, 3=don’t know, 2=not well, 1=not at all
3-3. Swabian People & Jokes: Do you know [various well-known Swabians]?
5=of course, 4=somewhat, 3=don’t know, 2=not well, 1=not at all
3-4. Swabian Activities: Do you participate in Hocketse & local activities?
5=always, 4=some, 3=don’t know, 2=not much, 1=never
Swabian Language Usage:
4-1. Parents Speak Swabian: Do your parent speak Swabian?
5=both, 3=one, 1=neither
4-2. Swabian with Friends & Family: Do you speak Swabian with …?
5=considerable, 4=some, 3=don’t know, 2=not much, 1=none
4-3. Swabian with Neighbors: Do you speak Swabian with …?
5=considerable, 4=some, 3=don’t know, 2=not much, 1=none
4-4. Swabian with Others: Do you speak Swabian with …?
5=considerable, 4=some, 3=don’t know, 2=not much, 1=none
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Annelise-1982: e echter Schwââb isch ôifach so ôiner, der sich als Schwââb fühlt. ‘a real Schwab is simply someone who feels like a Schwab.‘ Louise-2017: i bin e Schwââb und bleib ôiner. ‘I‘m a Schwab and will stay one.’
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Ricarda-1982: die Annie zum Beispiel, derer gfällt s net, wenn i schwäbisch schwätz. es würd net zu mir passe. sie meint schweitzerisch oder österreichisch würd besser zu mir passe. des würd sich so lätschig anhöre. ‘Annie for example, doesn‘t like it when I speak Swabian. It doesn‘t go with me. She thinks Swiss German or Austrian German would go better with me. It would sound so slouchy.‘ Pepin-2017: von dem her war i mal typisch, und zum Glück nimme so arg. ‘[As a real Schwab] I was typical, and luckily not so much anymore.’
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―Labovian-style, casual interview questions
―Native Swabian speakers ―Transcription Guidelines and Swabian Orthography ―Reviewed/Corrected by Principal Investigator
―Dialect Density Measure (DDM) ―Generalized Linear Mixed Models with Random Effects (GLMER)
―Quasi-Ethnographic Investigations
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show significant dialect attrition
across speakers in use of dialect variants in 2017
morpho-syntactic variables than phonological ones
DIALECT STANDARD
Legend: Orange – Women Blue – Men
+
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Random Effects lodds prob Speaker ID 0.2457 56.1% Interviewer Name 0.2421 56.0% Factors spkrs weight lodds prob diff merr sig Year 1982 13 .638 0.0275 50.7% 2017 13 .362 -0.7949 31.1% Orientation lowest (2.1) 11 .371 -0.8984 28.9% highest (4.5) 15 .629 -0.0604 48.5% Mobility lowest (1.0) 17 .585 -0.0475 48.8% highest (5.0) 9 .415 -1.0731 25.5% Gender Men 12 .444 -0.6201 35.0% Women 14 .556 -0.1862 45.4% Community Gmünd 14 .507 -0.1399 46.5% Stuttgart 12 .493 -0.6745 33.8%
19.6%
10.4%
11.9% 19.1% 4.1% 2.4% 2.7% *** *** *** . UNIVARIATE MAIN EFFECTS
n =
13 speakers 26 recordings 10 variables 22,559 tokens
Margin of Error:
Percent that the differences between the probabilities may vary up or down
Significance levels:
*** 0.001 ** 0.01 * 0.05 . 0.1
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Random Effects lodds prob Speaker ID 0.3874 59.6% Interviewer Name 0.3439 58.5% Year Gender spkrs lodds prob diff merr sig 1982 Men (Intercept) 6 0.0044 50.1% Women 7 0.0286 50.7% 2017 Men 6 -1.5527 17.5% Women 7 -0.4576 38.8% 0.6% 21.3% INTERACTION EFFECTS 19.2% 17.3% *
Page 17 Beaman – Language, Place and Periphery – University of Copenhagen – January 2018 Legend: Orange – Women in 1982 Red – Women in 2017 Green – Men in 1982 Blue – Men in 2017
merr sig % % % *** % *** Year Gender Orientation 1982 Men Low (mean 3.4) High (mean 4.1) Women Low (mean 3.6) High (mean 4.3) 2017 Men Low (mean 3.0) High (mean 4.0) Women Low (mean 3.0) High (mean 4.2) ds prob diff 65 49.3% 06 50.3% 36 47.2% 52 53.4% 97 12.2% 87 32.8% 05 23.3% 20 52.3% 20.5% 29.0% 0.9% 6.2%
Legend: Orange – Women in 1982 Red – Women in 2017 Green – Men in 1982 Blue – Men in 2017 Legend: Orange – Women in 1982 Red – Women in 2017 Green – Men in 1982 Blue – Men in 2017
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Year Gender Mobility spkrs lodds prob diff merr sig 1982 Men Low (mean 1.0) 5 0.0343 50.9% High (mean 1.9) 1 -0.1448 46.4% Women Low (mean 1.0) 7 0.0286 50.7% NA NA NA 2017 Men Low (mean 1.2) 2 -0.8711 29.5% High (mean 3.3) 4 -1.8935 13.1% Women Low (mean 1.2) 3 0.0910 52.3% High (mean 3.3) 4 -0.8692 29.5%
NA 8.5% *** 6.9% ***
11.2% NA
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Varbl Year n lodds prob diff merr sig STP 1982 2757 1.1690 76.3% 2017 3463 0.3504 58.7% AIS1 1982 2013 -1.6104 16.7% 2017 2741 -3.1309 4.2% ANN 1982 1756 -0.3135 42.2% 2017 1863 -1.4504 19.0% FRV1 1982 606 -0.8642 29.7% 2017 661 -2.3847 8.4% LEO 1982 575 -0.7152 32.8% 2017 978 -1.6766 15.8% -17.1% 11.4% **
9.8% ***
6.2% *** INTERACTION EFFECTS with RECORDING YEAR EDP 1982 544 2.3765 91.5% 2017 612 0.0266 50.7% IRV1 1982 227 0.5554 63.5% 2017 295 -1.5925 16.9% IRV3 1982 636 0.2719 56.8% 2017 1203 -1.7861 14.4% SAF1 1982 673 -0.3670 40.9% 2017 746 -1.1964 23.2% PVB 1982 103 0.7395 67.7% 2017 107 -1.2340 22.6% -45.1% 12.5% ***
Varbl Year n lodds prob diff merr sig INTERACTION EFFECTS with RECORDING YEAR
Phonological Variables Morpho-syntactic Variables
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Phonological Variables Morpho-syntactic Variables
EDP Gmünd 681 1.6137 83.4% Stuttgart 475 0.7207 67.3% IRV1 Gmünd 312 0.8337 69.7% Stuttgart 210 -2.0961 11.0% IRV3 Gmünd 990 -0.2737 43.2% Stuttgart 849 -1.3211 21.1% SAF1 Gmünd 855 -0.6207 35.0% Stuttgart 564 -0.9696 27.5% PVB Gmünd 134 0.9161 71.4% Stuttgart 76 -1.6045 16.7% -54.7% 26.2% ***
.
Varbl Community n lodds prob diff merr sig INTERACTION EFFECTS with COMMUNITY Varbl Community n lodds prob diff merr sig STP Gmünd 3395 0.9277 71.7% Stuttgart 2825 0.5637 63.7% AIS1 Gmünd 2601 -1.3583 20.5% Stuttgart 2153 -3.5518 2.8% ANN Gmünd 1982 -0.8483 30.0% Stuttgart 1637 -0.9213 28.5% FRV1 Gmünd 744 -0.6121 35.2% Stuttgart 523 -2.8056 5.7% LEO Gmünd 729 -0.6507 34.3% Stuttgart 824 -1.8320 13.8% -20.5% 19.7% *
INTERACTION EFFECTS with COMMUNITY
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Gmünd-1982 Stuttgart-1982 Gmünd-1982 Average-1982 Stuttgart-1982 Gmünd-1982 Average-1982 Gmünd-2017 Stuttgart-1982 Gmünd-1982 Stuttgart-2017 Average-1982 Gmünd-2017 Stuttgart-1982 Gmünd-1982 Average-2017 Stuttgart-2017 Average-1982 Gmünd-2017 Stuttgart-1982 Gmünd-1982
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Dialect Use in 2017 Attrition since 1982
Legend:
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―Rupert, Ph.D. sociologist and consultant, traveling nationally – 28% DDM attrition ―Angela, medical doctor specializing in childhood disabilities – 4% DDM attrition
―Ricarda, local kindergarten teacher in Stuttgart (Waldorf School) – 27% DDM attrition ―Elke, local kindergarten teacher in small town outside of Gmünd – 2% DDM attrition
―Markus, marketing manager for an IT company in Munich – 43% DDM attrition ―Annelise, medical doctor now working in Zurich – 21% DDM attrition
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such as Orientation and Mobility.
―The women are more influenced by Orientation ―The men are more impacted by greater Mobility
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CONTACT INFORMATION:
Karen V. Beaman Queen Mary, University of London Eberhard Karls University of Tübingen karenbeamanvslx@gmail.com +49 152 5468 7070
SPECIAL THANKS TO: Harald Baayen, Jenny Cheshire, James Garrett, Gregory Guy, Michael Ramscar, Fabian Tomaschek for review and feedback on the statistics, findings, and presentation; any deficiencies remaining are, of course, my own.
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Schwäbisch Gmünd
Rupert Angela Annelise Herbert Louise Elke Markus
Stuttgarter Area
Egbert Ema Pepin Ricarda Manni Bertha Rupert Angela Annelise Herbert Louise Elke Markus Egbert Ema Pepin Ricarda Manni Bertha
1982 2017
Karen Close Not-Close
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Recording Year: Orientation: Mobility: Gender: Community:
Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.20067 0.21001 0.956 0.339 dum_yr -1.19728 0.08346 -14.346 <2e-16 ***
Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.94419 0.21127 -4.469 7.85e-06 *** SOIR 1.11074 0.05039 22.041 < 2e-16 *** Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.07554 0.22835 -0.331 0.741 SMIR -0.66518 0.05643 -11.787 <2e-16 *** Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.5847 0.3128 -1.869 0.0616 . spk_genderW 0.4593 0.2972 1.545 0.1223
Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.2991 0.3656 -0.818 0.413 spk_communityStuttgart -0.1401 0.4709 -0.298 0.766
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Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.3031 0.5366 0.565 0.572191 dum_yr -0.6943 0.2516 -2.759 0.005790 ** spk_communityStuttgart -2.2155 0.5780 -3.833 0.000127 *** spk_genderW 1.1690 0.4125 2.834 0.004600 ** SMIR -0.3844 0.1950 -1.971 0.048683 * SOIR 0.5350 0.2199 2.432 0.014995 * spk_genderW:SOIR -2.0432 0.2399 -8.518 < 2e-16 *** spk_communityStuttgart:SOIR 2.1122 0.3151 6.702 2.05e-11 *** dum_yr:SOIR -0.1740 0.2543 -0.684 0.493970 dum_yr:spk_genderW 0.9682 0.1187 8.155 3.49e-16 *** spk_genderW:SMIR -0.3694 0.2138 -1.727 0.084079 .
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> LINGSUM <- glmer(data.matrix(data.frame(dsc$vard, dsc$vars)) + ~ AIS1 + ANN + # FRV1 # one dummy variable is redundant: it's at the intercept + + LEO + STP + EDP + IRV1 + IRV3 + PVB + SAF1 + + dum_yr + SOIR + SMIR + spk_gender + spk_community + + dum_yr:(SOIR + spk_gender) # SMIR + spk_community + + SOIR:(SMIR + spk_gender + spk_community) + + SMIR:(spk_gender) # spk_community + # spk_gender:(spk_community) + # AIS1:(dum_yr + SOIR + SMIR + spk_gender + spk_community) + + ANN :(SMIR + spk_gender + spk_community) # dum_yr + SOIR + # FRV1:(SOIR + spk_community) # dum_yr + SMIR + spk_gender + + LEO :(dum_yr + spk_community) # SOIR + SMIR + spk_gender + + STP :(dum_yr + SOIR + SMIR + spk_gender + spk_community) + + EDP :(dum_yr + SMIR + spk_gender + spk_community) # SOIR + + IRV1:(dum_yr + spk_community) # SOIR + SMIR + spk_gender + + IRV3:(dum_yr + SOIR + spk_community) # SMIR + spk_gender + + PVB :(SOIR) # dum_yr + SMIR + spk_community + spk_gender + + SAF1:(SOIR + SMIR + spk_gender + spk_community) # dum_yr + + (1|int_name) + + (1|spk_id) + , data=dsc, family="binomial" + , control=glmerControl(check.conv.grad=.makeCC("warning", tol=.002)) + )
Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.27847 0.53729 -0.518 0.604260 AIS1 -0.73628 0.08569 -8.593 < 2e-16 *** ANN -0.20498 0.11957 -1.714 0.086467 . LEO -0.31432 0.13730 -2.289 0.022064 * STP 1.61994 0.13088 12.377 < 2e-16 *** EDP 1.44973 0.16718 8.672 < 2e-16 *** IRV1 1.85313 0.23446 7.904 2.70e-15 *** IRV3 0.13740 0.18141 0.757 0.448805 PVB 0.83729 0.25390 3.298 0.000975 *** SAF1 0.60441 0.19022 3.177 0.001486 ** dum_yr -0.82867 0.26751 -3.098 0.001950 ** SOIR 0.68149 0.24615 2.769 0.005630 ** SMIR -0.88509 0.21761 -4.067 4.76e-05 *** spk_genderW 1.98651 0.43604 4.556 5.22e-06 *** spk_communityStuttgart -3.35954 0.56828 -5.912 3.38e-09 *** dum_yr:SOIR -0.70065 0.34664 -2.021 0.043249 * dum_yr:spk_genderW 1.06927 0.13407 7.975 1.52e-15 *** SOIR:SMIR 0.68166 0.20644 3.302 0.000960 *** SOIR:spk_genderW -2.37311 0.26688 -8.892 < 2e-16 *** SOIR:spk_communityStuttgart 2.05398 0.33640 6.106 1.02e-09 *** SMIR:spk_genderW -0.77474 0.27368 -2.831 0.004643 ** ANN:SMIR 0.71872 0.13195 5.447 5.12e-08 *** ANN:spk_genderW -0.65821 0.09945 -6.618 3.63e-11 *** ANN:spk_communityStuttgart 2.01474 0.12997 15.502 < 2e-16 *** LEO:dum_yr 0.58767 0.14823 3.965 7.35e-05 *** LEO:spk_communityStuttgart 1.07691 0.17350 6.207 5.41e-10 *** STP:dum_yr 0.21995 0.09724 2.262 0.023699 * STP:SOIR -0.19617 0.09614 -2.040 0.041304 * STP:SMIR 0.53622 0.12209 4.392 1.12e-05 *** STP:spk_genderW -0.55553 0.08863 -6.268 3.66e-10 *** STP:spk_communityStuttgart 1.48661 0.12342 12.045 < 2e-16 *** EDP:dum_yr -0.25834 0.16998 -1.520 0.128554 EDP:SMIR 0.19061 0.19240 0.991 0.321856 EDP:spk_genderW 0.42760 0.15102 2.831 0.004635 ** EDP:spk_communityStuttgart 0.88913 0.17467 5.090 3.58e-07 *** IRV1:dum_yr -0.73451 0.26352 -2.787 0.005315 ** IRV1:spk_communityStuttgart -0.80081 0.30623 -2.615 0.008922 ** IRV3:dum_yr -0.23498 0.13342 -1.761 0.078213 . IRV3:SOIR 0.52825 0.17611 2.999 0.002705 ** IRV3:spk_communityStuttgart 1.43130 0.16392 8.732 < 2e-16 *** PVB:SOIR 1.18045 0.36047 3.275 0.001057 ** SAF1:SOIR -0.40019 0.16576 -2.414 0.015768 * SAF1:SMIR 0.82096 0.17493 4.693 2.69e-06 *** SAF1:spk_genderW -0.71826 0.14364 -5.000 5.72e-07 *** SAF1:spk_communityStuttgart 1.40263 0.16827 8.336 < 2e-16 ***
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men women Legend:
ID Name Community SOI SMI lodds prob n SOI SMI lodds prob n S007 Egbert Stuttgart 4.0 1.0
49.1% 483 3.6 3.6
21.4% 1005
S008 Rupert Gmünd 4.0 1.1 0.2907 57.2% 1110 2.6 1.0
29.5% 1337
S010 Angela Gmünd 4.5 1.0 0.2494 56.2% 754 4.2 3.0 0.095 52.4% 1129
S011 Herbert Gmünd 4.2 1.9
46.4% 798 4.2 1.5
29.5% 1366
S012 Elke Gmünd 4.2 1.0 0.0415 51.0% 618 4.3 1.1
49.2% 882
S013 Louise Gmünd 4.3 1.0
44.9% 838 4.0 1.6 0.2458 56.1% 876 11.2% S014 Markus Gmünd 4.3 1.0 0.4498 61.1% 787 2.8 5.0
18.4% 764
S015 Ricarda Stuttgart 3.5 1.0
36.6% 810 2.1 4.1
9.2% 1160
S016 Manni Stuttgart 4.0 1.0
37.6% 862 3.0 1.9
1.5% 1037
S018 Pepin Stuttgart 3.4 1.0
49.3% 426 3.8 2.6
36.2% 900
S027 Annelise Gmünd 3.5 1.0 0.3871 59.6% 745 3.6 4.1
38.3% 419
S034 Bertha Stuttgart 3.7 1.0
45.6% 682 3.3 1.8
30.9% 1104
S040 Ema Stuttgart 4.2 1.0 0.4548 61.2% 977 4.2 1.0 0.0583 51.5% 690
2017 1982 difference bet years
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variability
―Large urban area with greater external influences ―Some speakers use zero dialect variants in 2017 for some variables ―Fewer number speakers (5 versus 7)
homogeneity
―Mid-sized town & rural communities ―Exception of Herbert who travels regionally & accommodates a lot
Community Name lodds weight Egbert Stuttgart
.495 Rupert Gmünd
.465 Angela Gmünd 0.3776 .547 Herbert Gmünd
.455 Elke Gmünd 0.2100 .526 Louise Gmünd 0.1990 .525 Markus Gmünd
.470 Ricarda Stuttgart
.417 Manni Stuttgart
.390 Pepin Stuttgart 0.4577 .557 Annelise Gmünd 0.1719 .521 Bertha Stuttgart 0.2060 .526 Ema Stuttgart 0.8731 .607