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Record W2770669163 · doi:10.1121/1.5014761

Northern vowel features within the deep south: The new orleans yat dialect

2017· article· en· W2770669163 on OpenAlexaboutno aff
Katie Carmichael

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsRaising (metalworking)VowelDiphthongLinguisticsRealization (probability)HistoryAmerican EnglishReading (process)Sound changeGeographyMathematics

Abstract

fetched live from OpenAlex

Linguists have noted the distinctively Northern sound of the White, working class New Orleans English dialect locally referred to as Yat, though the features of this dialect have not been well documented phonetically. Vowel features attested include a split short-a system, BOUGHT raising, and Canadian raising of /ɑʊ/—common to Northeastern dialects but rare within the American South. In this study, I analyze the realization of these sounds within a corpus of interviews, reading passages, and word lists completed with 57 local Yat speakers, situating Yat within broader American English trends. Results indicate that in comparison to descriptions of these features in Northeastern dialects, the Yat split short-a system features slightly different phonetic triggers, BOUGHT is less raised and diphthongal, and Canadian raising appears to occur across the diphthong and not just in the nucleus. Thus, while these features do resemble those found in Northeastern dialects, there are some local distinctions. Moreover, BOUGHT raising and the split short-a system are in decline amongst younger speakers, while Canadian raising is a relatively recent innovation within the New Orleans area. Thus, Yat will likely remain a peculiar dialect within the South, despite its changing qualities.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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