Acoustics of stress and weight in Central Alaskan Yup’ik
Bibliographic record
Abstract
In Central Alaskan Yup’ik, syllables with long vowels are always stressed, light syllables alternate stress, but only certain closed syllables are stressed. The acoustic correlates of stress, however, have only been the subject of one small-scale preliminary study so far. Moreover, there are divergent accounts of how phonological phenomena such as gemination and syllable closure affect weight. This article presents an acoustic investigation of gemination, stress, and phonemic length. Six Yup’ik recordings were annotated, resulting in a dataset of 2,602 syllable onsets and 2,282 vowels, which were then modelled using linear mixed-effects models. The first part of the study, examining the distribution of gemination as a metrical-adjacent phenomenon, revealed that singleton onsets were shorter than geminated onsets both within feet and across foot boundaries. The main study showed that stressed vowels were longer, louder, and, for short vowels only, higher in f0 than unstressed vowels, while long vowels were longer, louder, and featured greater f0 falls than short vowels. These results corroborate the literature that asserts that long, short unstressed, and short stressed vowels are all produced distinctively, and moreover, that vowel duration is affected by iambic lengthening and syllable closure. The identification of stress correlates and other metrical behaviors examined here set the stage for future prosodic work on Yup’ik.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".