Patterns of pre-nasal allophony across dialects of English: A multi-corpus study of the /ɪ/-/ɛ/ contrast
Bibliographic record
Abstract
We present a study of pre-nasal allophony across a variety of English dialects from North America and the UK in order to investigate the degree to which pre-nasal allophony behaves categorically or gradiently in English. We focus on the contrast between /ɪ/ and /ɛ/, which undergoes allophonic merger in the Southern US and has a marginal F1/F2 contrast in Scottish English. There are two attested types of pre-nasal allophony in English: pre-nasal coarticulation and pre-nasal merger. Both types of allophony are predicted to have the effect of bringing /ɪ/ and /ɛ/ closer together in F1/F2 space. We compare the amount of overlap between /ɪ/ and /ɛ/ in pre-nasal contexts to the pre-oral "baseline" overlap and ask whether dialects and individual speakers fall along a gradient with respect to pre-nasal vs. pre-oral overlap. We find that dialects fall roughly into two groups, but there is evidence of dialects falling in between the two groups. These findings support a "hybrid" view of pre-nasal allophony, in which behavior is roughly categorical, but dialects can exhibit intermediate, gradient behavior. Within dialects, interspeaker variation shows more evidence for gradient behavior, although only two dialects exhibit what appears to be a full gradient in pre-nasal allophony.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".