Bibliographic record
Abstract
The idea of a language-specific articulatory setting (AS), an underlying posture of the articulators during speech, has existed for centuries [Laver, Historiogr. Ling. 5 (1978)], but until recently it had eluded direct measurement. In an analysis of x-ray movies of French and English monolingual speakers, Gick et al. [Phonetica (in press)] link AS to inter-speech posture, allowing measurement of AS without interference from segmental targets during speech, and they give quantitative evidence showing AS to be language-specific. In the present study, ultrasound and Optotrak are used to investigate whether bilingual English-French speakers have two ASs, and whether this varies depending on the mode (monolingual or bilingual) these speakers are in. Specifically, for inter-speech posture of the lips, lip aperture and protrusion are measured using Optotrak. For inter-speech posture of the tongue, tongue root retraction, tongue body and tongue tip height are measured using optically-corrected ultrasound. Segmental context is balanced across the two languages ensuring that the sets of sounds before and after an inter-speech posture are consistent across languages. By testing bilingual speakers, vocal tract morphology across languages is controlled for. Results have implications for L2 acquisition, specifically the teaching and acquisition of pronunciation.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".