Durational and spectral factors in judgements of American Raising
Bibliographic record
Abstract
Canadian Raising and its relatives in the USA can respond to underlying voicing of flapped /t/ (Raised “writer” vs. un-Raised “rider") from the earliest stages (Fruehwald 2016). How so, if Raising is phonologized from a phonetic precursor sensitive only to phonetic features? Proposal (Bermúdez-Otero 2019): Raising responds to duration, not voicing: diphthongs are shortened before underlyingly voiceless sounds by a pre-existing lexical phonological rule of Pre-Voiceless Clipping, after which postlexical Raising transparently affects the shortened diphthongs. Experiment: Participants (N = 141, US dialects) read wordlists with /ai/ and /ei/ in voiceless and voiced contexts, then sorted the words into groups judged to share a vowel (DiPaolo & Faber, 1990). Clipping and Raising were measured using duration, F1, and F2. Predictions: (1) across speakers, /ai/-Clipping and / ai/- Raising should be positively correlated. (2) /ai/-sorting should be better predicted by /ai/-Clipping than by /ai/-Raising, because lexical rules change phonemes, while postlexical ones are subphonemic. (3) /ei/-sorting should be positively correlated with /ai/-sorting, since Clipping affects all vocoids. Results: (1) /ai/-Clipping did *not* predict /ai/-Raising (r = −0.082); (2) / ai/-Clipping predicted word sorting marginally *worse* than /ai/-Raising (95% CI for rclipping-rraising = (−0.50, 0.03)). (3) /ei/ and /ai/ judgements *were* positively correlated (r = 0.37, 95% CI = (0.21, 0.51)).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".