Bibliographic record
Abstract
The confusability of sounds is argued to both reflect phonological structure [e.g., Boomershine et al. (2008)] and be a source of phonological variability and change [e.g., Ohala (1981), Hume (1998)]. We present the results of a perception task in which 25 Parisian French-speaking participants identified the French vowels [i e ɛ y o/ œ ə a u o ɔ ɔ̃ ɛ̃ ɑ̃], or ∅, in an aC_Ca context, using standard orthography in key words. The durations of the vowel in the vowel-containing stimuli were manipulated to be 0, 15, 25, 50, or 100% of their original durations. We can therefore determine which vowels are most confusable with each other (and thus likely to be the target for either mergers or dissimilatory processes) and which are most confusable with zero (and thus likely to be the target of processes such as deletion, assimilation, and metathesis). Results show high accuracy for [a i y u], even with very short durations; some degree of confusability within the nasal vowels; high confusability rates within the mid-front rounded vowels; and a tendency for zero to be confused with one of the mid-front rounded vowels. These results align with observed phonological patterns in French.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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 teacher head, 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".