An EPG study of palatalization in French: Cross-dialect and inter-subject variation
Bibliographic record
Abstract
This article studies palatalization gestures in the production of /t/ and /d/ in standard Belgium French through the use of electropalatography. The articulatory results are compared with an acoustic study of the affricated realization of these consonants when followed by /i/, /y/, /j/, and /[inverted h]/ in Quebec French (Bento, 1993). The study examines regional and individual differences in palatalization gestures to show how affrication can be ascribed to palatalization. Results are analyzed with regard to temporal, articulatory (electropalatography), and voicing aspects to compare production strategies across French variants, speakers, and phonetic contexts. The aim is to understand coordination processes, which are explained in terms of biomechanical constraints resulting from the coordination of adjacent gestures. These processes are used to show how palatalization traces in a variant with no recognized affrication (Belgium French) are similar in nature to the affrication in Quebec French, because palatalization is due to the coarticulation of the stop with a following high front vowel or a palatal approximant. Results are also compared with diachronic data to propose explanations for some patterns in the development from Latin to French.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".