Sibilant production and perception in the North American West
Bibliographic record
Abstract
This paper explores variability in English /s/-/ʃ/ production and perception across the North American West. Although this dialect region has long been considered monolithic, recent work has begun to explore the phonetic diversity within the region. However, much of this work has investigated vocalic variation, and sibilants have remained relatively underexamined. Given reports of /s/-retraction as a change-in-progress in varieties of English around the world (particularly in /str/ clusters), sibilants are of particular interest. In this study, monolingual participants from Oregon, California, and British Columbia provided single word productions and performed a /s/-/ʃ/ perception task. Preliminary results indicate the prevalence of /str/ retraction in the region, with most retraction in speakers from California, followed by Oregon, followed by British Columbia. Region-internal differences in perception of /s/-/ʃ/ were minimal, though a speaker’s /str/ retraction ratio contributed to predicting perception for speakers with smaller distances between /s/ and /ʃ/, consistent with prior work finding more perceptual similarity for speakers for whom two sounds are in an allophonic relationship. Taken together, these results contribute to the description of speech in the western region of North America, as well as add data to ongoing questions about variation in production, perception, and their relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".