Bibliographic record
Abstract
The coda-voicing effect on vowel duration (CVE), in which vowels are longer with voiced codas and shorter with voiceless, is well attested. However, the precise mechanism of this relationship is not well studied. Task-dynamic models such as articulatory phonology (e.g., Browman & Goldstein, 1986) offer two ways to affect the duration of a vowel in a CVC syllable: the relative phasing of the onset, vowel, and coda gestures (when they start relative to one another), and the relative stiffness of an individual gesture (roughly how quickly it is executed). Onosson (2010) argued that for /ai/, the principal mechanism of Canadian Raising (CR) was vowel shortening via increased overlap of the onset gesture and the vowel, rather than vowel-coda phasing or stiffening the vowel gesture. This study investigates whether this explanation holds generally for 15 vowels in Canadian English, using controlled wordlist-style data (originally from Hagiwara, 2006). Preliminary investigation suggests that onset-phasing may hold for the diphthongs, but does not adequately characterize CVE across the vowels. The observable mechanism(s) of CVE will be discussed, as well as implications for CVE and related effects (e.g., CR) across dialects/languages generally.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".