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Record W4210297146 · doi:10.16995/labphon.6453

“Dialect B” on the Mississippi: An acoustic study of /aw/ raising patterns in Greater New Orleans, Louisiana

2022· article· en· W4210297146 on OpenAlexaboutno aff
Marie Bissell, Katie Carmichael

Bibliographic record

VenueLaboratory Phonology Journal of the Association for Laboratory Phonology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
FundersOhio State University
KeywordsRaising (metalworking)VoiceVariety (cybernetics)American EnglishLinguisticsObstruentDiphthongVowelPhoneticsPsychologyHistoryMathematicsComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

“Dialect B,” a diphthong raising pattern conditioned by a following obstruent’s surface voicing, was first observed by Joos (1942) among Canadian schoolchildren. It has rarely been documented for /ai/ (Berkson, Davis, & Strickler, 2017) and has never been documented for /aw/ in any North American English variety. Phonetic /aw/ raising, which has raised nuclei in words like “out” but not in words like “loud” or “outer,” contrasts with more widely documented phonological /aw/ raising, which has raised nuclei in words like “out” and “outer” but not in words like “loud.” In the current study, we examined /aw/ productions from 57 white suburban speakers of Greater New Orleans English, a variety where /aw/ raising before voiceless consonants is a change in progress (Carmichael, 2020b). We classified speakers into three raising patterns: none, phonetic, and phonological. All three raising patterns were present in our data set. This study thus constitutes the first acoustic documentation of a phonetic /aw/ raising pattern produced by a North American English speaker. Additionally, we probe the acoustic implementations of the patterns to analyze phonetic enhancement post-phonologization. These analyses add to descriptions of Greater New Orleans English patterns and build on recent work examining incipient vowel shifts.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.301
Teacher spread0.269 · 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 designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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