jeff connnwav34 photo by john frank keith of moice and men: of moice and men: the evolution of a...

Jeff ConnNWAV34

Photo by John Frank Keith

Of “moice” and men:Of “moice” and men: The evolution of a male-led sound change

Jeff ConnNWAV34 Of “moice” and men: The evolution of a male-led sound change

Sociolinguistic studies show language change led by:

Women

The interior social classes

Supported by the data from the study of Linguistic Change and Variation in Philadelphia [LCV] (Labov, 2001)

The Curvilinear Principle: Linguistic change from below originates in a central social group, located in the interior of the socioeconomic hierarchy (188)

Conformity Paradox: Women deviate less than men from linguistic norms when the deviations are overtly proscribed, but more than men when the deviations are not proscribed (367)


Problem variable in the LCV data: The raising of the nucleus of the diphthong /ay/ before voiceless consonants (ay0)

Led by men

Shows no social stratification

Is (ay0) a counter-example to “typical” language change?

How does (ay0) progress through the speech community over time?

What about the movement on the front/back dimension of (ay0)?

If (ay0) does not behave like other vocalic changes in progress, are there certain gender-based evaluations of this variable? That is, do certain variants sound more masculine/feminine?

Questions from the patterning of (ay0) in the LCV data:

psychnice man


OMM:

Re-study of Philadelphia 30 years after LCV

Data collected from (2000-2003)

Focus on (ay0) and secondary focus on (aw)

Included self-identified gays and lesbians as part of the data set

The current study: Of “moice” and men: The evolution of a male-led sound change [OMM]

Striving for high comparability with the original study, OMM followed the methodology and data analysis of the LCV as discussed in Labov, 2001Microphone and recorder differences were not taken into consideration, but will be looked into in the future


MethodologySample: 65 native Philadelphians

The data: sociolinguistic interviews (at subject’s house) including formal tasks of semantic differentials, minimal pairs tests, reading passage and a word list

Social Coding: Each speaker was coded for various social characteristics following the LCV (see Labov, 2001 for further details) - education, occupation and residence converted into socioeconomic class category (SEC)

age sex education occupation residence value mobility

house upkeepethnicityforeign language backgroundgenerationneighborhood of origin


Methodology

Also coded for sexual orientation

LWC UWC LMC UMC

Men 4 4 8 8Women 7 6 6 8

Gay Men 2 3Lesbian Women 4 5

Sexual orientation for both F1 and F2 (ay0) is not a significant social factor predicting values as either a binary category (gay/lesbian~hetero) or a combo 4-way split of sex and sexual orientation

Binary Category Sex/Sexual Orientation Combo

F1 F2 F1 F2

p < . 0.9478 (F2) p < 0.5843

p < 0.6660 p < 0.3294


Methodology

Acoustic vowel analysis

LPC analysis in Praat

Single-point, synchronous nuclear measurements of F1 and F2

Additional auditory support for single-point selection

Vowels of all Plotnik 25 vowel classes were measured - at least 5 tokens per class per speaker - complete vowel system for every speaker (200-500 tokens)

Data cleaned for measurement errors

Using Neary’s Log mean normalization in Plotnik, each speaker’s cleaned system was normalized, and from these data, a mean F1/F2 for each vowel class (and phonetic subclasses) was calculated


Methodology

Some methodological concerns for investigating a speech community in real time 3 decades later

Subject recruitment: representative neighborhoods have changed

Updating the socioeconomic class index (SEI)


Methodology

Subject recruitment:

LCV neighborhoods selected to represent different social classes - Kensington (NE), South Philly (S), Overbrook (W) & King of Prussia (NW)

OMM neighborhoods sampled similar areas - biggest change was substituting Chestnut Hill/Mount Airy for King of Prussia

Self-identified gays and lesbians recruited through personal contacts (sometimes relatives/friends of neighborhood subjects)

Neighborhood area of Philadelphia

South West North Northwest Northeast CenterMen 9 4 3 4 8 1

Women 14 4 2 7 9


Methodology

Updating the socioeconomic class index (SEI):

LCV used scale below to calculate socioeconomic score, which was used to calcluate socioeconomic class category (SEC)

Education (E)6 professional school5 college grad4 some college3 high school grad2 some high school1 grammar school

Occupation (O)6 professional, owner director of large firm5 white collar – proprietor, manager4 white collar – merchant, foreman, sales3 blue collar – skilled2 blue collar – unskilled1 unemployed

Residence Value (R)6 $25,000+5 $20,000 – $24,9004 $15,000 – $19,9003 $10,000 – $14,9002 $5,000 – $9,9001 $0 – $4,900


Methodology


The median residence values according to the census data have increased from $10,600 (1970) to $59,700 (2000), so each level of the residence scale was multiplied by 5.632 to reflect this change

According to the 1970 and 2000 censuses, the median education attainment level changed from 10.9 years in 1970, to graduating high school or equivalent in 2000. This reflects an overall increase in the population’s education, so 1 point was added to each SEC to account for this.


Methodology


LCV ClassCategory

SEIScore

OMM ClassCategory (SEC)

SEIScore

LWC 2-3 MWC 4-6

LWC 3-7

UWC 7-9 UWC 8-10LMC 10-12 LMC 11-13UMC 13-15 UMC 15-18UC 16 -- --

Translation of social class categories (SEC) from LCV to OMM


Statistical AnalysisIn order to examine all the independent variables at the same time, a stepwise multiple regression analysis was conducted using the following independent social variables:

age sex education occupation residence value mobility

house upkeepethnicityforeign language backgroundgenerationneighborhood of origin


Apparent Time F1 (ay0) Results

The stepwise regression analysis of (ay0) selected the following social variables as significant factors in predicting F1 (ay0) values

age

occupation generation



This model with age, occupation and generation can account for 46% of the variation (r2 = 0.46) of F1 (ay0) in the data, with age as a significant predictor at p < .0001

Data show change in apparent time

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Pre

dict

ed F

1 (a

y0)

age groups



Generation score of 3 significantly higher F1 (ay0) values (non-raised variants) than the other scores

Occupation score of 3 has significantly lower F1 (ay0) values, while a score of 4 has significantly higher F1 (ay0) values (not curvilinear principle)

Pre

dict

ed F

1 (a

y0)

occupation scores

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1 2 3 4 5 6

Occupation scores based on regression estimates (least squares means)



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ay01

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Age

LWC

UWC

LMC

UMC

Apparent time shows no sex differentiation or social stratification

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ay01

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Age

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Sex not significant social factor predicting F1 (ay0) - distribution shown below (linear fit lines and p = .90 bivariate normal elipses)

Sex = Female

Sex = Male

SEC not significant social factor predicting F1 (ay0) - distribution shown below

Regression lines for each social class of F1 (ay0) with age as a continuous variable



F2 (ay0) does not show age as significant social factor predicting F2 values (no change in apparent time)

SEC does show significant effects (p<.001), but when sorted by sex, only men show significant social stratification (p<.001) while women do not (p>.10)

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1200130014001500

F2

F1

LWC Men

UWC Men

LMC MenUMC Men

UMC Women

UWC Women

LWC Women

LMC Women

Predicted F1/F2 (ay0) values plotted by sex and social class


OMM: Real Time F1 (ay0) Results

Transformed LCV data into comparable age groups with OMM

F1 (ay0) in apparent time for both data sets

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LCVOMM

Pre

dic

ted

F1 (

ay0

)

age group

Jeff ConnNWAV34

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Of “moice” and men: The evolution of a male-led sound change

Real Time F1 (ay0) Results

Added 30 years to LCV ages and combined data sets

Stepwise process selected age and sex as significant social factors (at the p < 0.1 level) with 33.7% of variation explained by model (r2 = .337)

Real time change shows larger decreases in F1 (ay0) followed by plateaus of little change

Pre

dic

ted

F1 (

ay0

)

age group

Predicted F1 values of (ay0) for both LCV and OMM data sets

Jeff ConnNWAV34

WomenMen

Of “moice” and men: The evolution of a male-led sound change


Sorting the data by sex, varying moments of sex differentiation

This picture is different from apparent time analysis in Labov, 2001 in that unified speech community in 80-89 age group

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WOMEN:Age coefficient = 1.46r2 = 0.348

MEN:Age coefficient = 1.44r2 = 0.260

Predicted F1 (ay0) values for combined data sets sorted by sex

Pre

dic

ted

F1 (

ay0

)

age group


Real Time F1 (ay0) ResultsSEC not selected as significant factor in the regression model, but sorting the data by SEC, age still a significant factor (p < .10 level) for each class (change occurring in all classes)

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F1

ay0

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Age

Regression lines for each social class of F1 (ay0) for both studies

LMC

UWC

LWC

UMC



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Predicted F2 (ay0) values by age groups for combined data

F2 (ay0) in combined data set - stepwise regression model selected age, occupation, residence and education as significant social factors (p <.10)

Pre

dic

ted

F2 (

ay0

)

age group



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LWCUWCLMCUMC

Predicted F2 (ay0) values for both data sets by age group and SEC

Pre

dic

ted

F2 (

ay0

)

age group

Real time analysis does not show clear social stratification of this change

Sorting the data by SEC, age only significant factor in LWC (p < 0 .0354) and UWC (p < 0.0205)


Real Time SummaryThe mechanism of (ay0) raising sound change: change began by whole community, and then sex differentiation

No clear social stratification of this variable

Only real time analysis shows F2 backing over time

F2: no sex differentiation, but social class stratification


Subjective Reaction Test

Table 6.1 SRT sentences by variable

Variable Sentence Name

Neutral It was a lot different from what we expected. DiffNeutral We bought some equipment a couple weeks before we left. Equip

(ay0)It was quite a fight, trying to put in the two big pipes, but wefinally did it.

Fight

(ay0) It was a fine sight; we got a bite to eat and got to sleep by nine. Sight

(aw)We scouted around for wood, and found some without muchtrouble.

Scout

(aw)We took down the tent and set out toward a mountain abouttwo hours south of us.

Mount

6 “Speakers”:

Jill - 24 year old woman; Ben - 43 year old man (2 guises each)

1 other man and 1 other woman used as fillers

Sentences

3 variables investigated (aw, ay0, and neutral) X 2 sentences each


Subjective Reaction TestCreating the test:

Both Jill & Ben practiced so they produce moderate Philadelphia tokens and more extreme Philadelphia tokens (extra Philly)

Jill/Ben’s vowel system calculated through reading passage and word list

Tokens for each guise selected from the many possibilities comparing the extra Philly tokens within each speaker’s “regular” vowel system

Sentences spliced together from the selected tokens

Sentences were duplicated (so each sentence played two times consecutively) and randomized

Male then female speaker alternating

Used filler speakers to make sure that no two identical sentence of the corresponding Jill/Ben guise occurred close together


Subjective Reaction Test

Table 6.2 SRT scales

Job suitability Toughness

What is the highest job this person couldhold, speaking as (s)he does?

If this speaker got into a fight, howlikely is it that (s)he would win?

No jobat all 1 2 3 4 5 6 7 Television

Personality

Not atall

likely1 2 3 4 5 6 7

Verylikely

Masculinity/Femininity Friendliness

How masculine or feminine do you thinkthis speaker is, speaking as (s)he does?

(4 = Average/Typical)

If you got to know this speaker well,how likely is it that (s)he wouldbecome a good friend of yours?

VeryFeminine 1 2 3 4 5 6 7 Very

MasculineNo at

all likely 1 2 3 4 5 6 7 Verylikely

Administering the SRT

36 sentence SRT administered as part of socioling interview (sometimes after, sometimes before)

Evaluators were told to rate each speaker (3 men, 3 women) on the following scale for each sentence.


Subjective Reaction Test3 Analyses of SRT evaluations

1) looks at the data from all of the evaluators to see if patterns from the matched guise aspect are revealed from the entire speech community

2), following the analysis of the LCV SRT in Labov, 2001, examines the difference for each speaker/guise from the neutral sentence ratings to the ratings of each variable

3) uses a series of differences in each evaluator’s ratings to uncover any social variables which may affect the ratings


Subjective Reaction TestAll evaluators matched guise evaluations

2-tailed, unequal variance t-tests conducted on mean evaluations for matched guises (boxed diffs are significant at p < 0.01) Variable: (ay0)

3.94.1

2.62.7

3.43.4

2.62.3

4.1

4.8

3.9

5.15.5

4.94.8

4.0

1

2

3

4

5

6

7

Job Friend Tough M/FScale

Ra

tin

g Jill

Jill2

Ben

Ben2


Subjective Reaction TestAll evaluators intraspeaker evaluations

Jill/Jill2: (Æ) - (ay0)

4.4

2.62.6 2.62.7

3.9 3.94.1

2.8

3.4

2.9

3.8

4.2

2.52.5

4.3

1

2

3

4

5

6

7


Ra

tin

g Jill(ay0)

Jill(Æ)

Jill2(ay0)

Jill2(Æ)

Ben/Ben2: (Æ) - (ay0)

5.1

3.9 4.0

4.8

4.1

4.8

5.6

4.7

3.7

4.9 4.9

5.5

5.1

4.5

3.8

4.5

1

2

3

4

5

6

7


Ra

tin

g Ben(ay0)

Ben(Æ)

Ben2(ay0)

Ben2(Æ)


Subjective Reaction TestSocial factors of evaluators

Some significant factors, but not consistent

Age or sex never significant

Uniform speech community as evaluators


Subjective Reaction TestSRT Summary

Uniform speech community

Male and female speaker for (ay0) evaluated on different scales but male and female evaluators agree on this distinction and difference in sociolinguistic expectations of men and women


SUMMARY

Real time support for apparent time analysis of LCV (ay0)

(ay0) backing only shown in real time

This variable shows language change progresses not linearly, but taking large steps forward, and then relative stability

Sex differentiation not a given, but needs to be maintained at each step in the change


OMM: To be continued

What’s next? (To be continued at NWAV35...)

While (ay0) does not show sex differentiation or social stratification, the other new and vigorous changes do (eyC) and (aw)

They also show a significant effect of sexual orientation

What about other changes - incipient, completed? - in Philadelphian English

Is Philadelphia becoming a Northern city and losing its Philly-ness? What does this all mean?

Check out my website to download this presentation and find out more details about methodology: www. jeffconn.net

jeff connnwav34 photo by john frank keith of moice and men: of moice and men: the evolution of a...

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