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Record W4312594227 · doi:10.1121/10.0016283

Vowel trajectories of African Americans in Georgia, USA

2022· article· en· W4312594227 on OpenAlexaboutno aff
Margaret E. L. Renwick, Jon Forrest, Lelia Glass, Joey Stanley

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsVowelFormantContrast (vision)Secular variationRaising (metalworking)American EnglishQuarter (Canadian coin)Tone (literature)MathematicsGeographyDemographyLinguisticsSpeech recognitionComputer scienceSociology

Abstract

fetched live from OpenAlex

Within the United States, dialectal variation is often characterized by vowel shifts: systematic differences in vowels' relative qualities and vowel-inherent dynamics. The African American Vowel Shift (AAVS), in particular, includes raising and fronting of front lax /ɪ ɛ æ/, among other features. The more recent pan-regional Low Back Merger Shift (LBMS), by contrast, includes lowering and backing of the same vowels. We evaluate these two shifts in an audio corpus of over 40 Black speakers from the Southern state of Georgia. Speakers, born between the 1930s and 2000, represent five demographic generations. Normalized formant values (F1,F2) from five temporal points per token are input to Generalized Additive Mixed Models (GAMMs). We test for significant changes in vowels' trajectories across time by fitting Year of Birth as a continuous smooth term. Additionally, we use linear mixed-effects modeling to test for raising versus lowering on the (F2–F1) front-vowel diagonal, across generations. Evidence from GAMMs and linear modeling indicates raised positions of /ɪ ɛ æ/ among older generations (1950s–1980s), followed by significant retraction from 1990–2000. These acoustic results are consistent with strengthening of the AAVS in the third quarter of the 20th Century, followed by a rapid transition to the pan-regional LBMS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.294
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicLinguistic Variation and MorphologyFrench-language works237,207