To appear in: Toronto Working Papers in Linguistics, 20, Daniel Hall (ed.) Testing Licensing by Cue
Bibliographic record
Abstract
This paper tests the hypothesis Licensing by Cue (Steriade 1997) applying it to the distribution of Russian plain-palatalized contrast in coronal stops /t / vs. /t j / in two environments (V_C, V_#). The hypothesis holds that the maintenance of the contrast should correspond to more acoustic information in the signal and higher identification rate, and that its neutralization should be accompanied by fewer cues and lower recognition of the segments. The results of acoustic and perceptual experiments do not fully support the hypothesis: while the relative acoustic and perceptual salience of the contrast before three consonants (_#k, _#n, _#s) correlates with within-word neutralization patterns, the lack of neutralization after three vowels (a_, u_, and i_) does not follow from the acoustic and perceptual results. The findings suggest that acoustic and perceptual factors play a certain role in maintenance and neutralization of phonological contrasts, however, the mapping between acoustics and phonology is not direct. 1
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.125 | 0.034 |
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".