Bibliographic record
Abstract
Merged production of /ɪ/ and /ɛ/ before nasal consonants is well documented in Southern US English. Perception studies of this merger are more limited (cf. Austen 2020). One possible source of pre-nasal merger is anticipatory vowel nasalization. A 2AFC perception task asked US listeners from inside or outside the South, to categorize stimuli on continua from bid to bed and bin to Ben. To test the effects of consonant and vowel nasality separately, we cross-spliced stimuli in a 2 by 2 design. We ask (1) whether Southern speakers are merged in perception, as is generally assumed, and (2) whether it is vowel nasality, consonant nasality, or both that gives rise to the merger in perception. We fit a logistic regression model on the probability of /ɛ/ responses as a function of continuum step, vowel nasality, consonant nasality, and subject region, with all interactions. We found that Southern listeners had a flatter categorization function with lower accuracy at the continuum ends than non-Southern listeners whenever a nasal coda was present, regardless of vowel nasality, confirming that (1) Southern speakers are, to some degree, merged in perception, and (2) that the presence of the nasal coda, and not vowel nasality, conditions merger in perception.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".