“Dialect B” on the Mississippi: An acoustic study of /aw/ raising patterns in Greater New Orleans, Louisiana
Bibliographic record
Abstract
“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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".