Bibliographic record
Abstract
Some speakers of Canadian French produce words such as pneu, un, and coeur with rhotic-sounding vowels similar to English /ɹ/ (Dumas 1972). Articulatory imaging [Mielke (2011)] shows that they are produced with bunched and retroflex tongue postures and low F3, much like English /ɹ/. Nevertheless, native speakers typically are completely unaware of the difference, even when it is pointed out to them. We report a preliminary perception study of rhotic vowels. 7735 words with mid front round vowels were coded as “rhotic,” “non-rhotic,” or “ambiguous” by two listeners: a French-English bilingual from eastern Ontario and an American English speaker. The bilingual coded 0.3% as rhotic (vs. 10.0% for the anglophone) and 7.3% as ambiguous (vs. 8.9%). Logistic regressions show that the anglophone relied on F3 to distinguish rhotic+ambiguous tokens from non-rhotic tokens, while the bilingual weighted several cues about equally, including F1 cues to diphthongization, which can co-occur with rhoticity. Results will be presented from an ongoing AX discrimination task experiment involving rhotic, non-rhotic, and ambiguous vowel tokens, with francophone, bilingual, and anglophone listeners from the Ottawa-Gatineau region, Paris, France, and Raleigh, North Carolina.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.007 | 0.001 |
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".