Pathways to depalatalization of the palatal nasal in Quebec and hexagonal French: An EPG study
Bibliographic record
Abstract
Abstract The palatal nasal is one of French’s most variable consonants with attested variants including [ɲ] alongside [nj] and, less frequently, [n] and [ŋ]. Variation is conditioned by both linguistic (position in the word, lexical item, flanking vowels) and speaker variables (in particular, variety). Except for insights provided by the studies reviewed in Recasens (2013), little is known of the articulatory properties of French /ɲ/ including the degree of inter-varietal and -speaker variation or the proportion of coronal and velar depalatalized realizations. We present here an electropalatographic (EPG) study of two European (EF) and two Quebec French (QF) speakers’ /ɲ/ production in both word-medial and -final positions in isolated and contextualized words. Quantitative indices and qualitative investigation of the linguopalatal contact profiles reveal that the EF speakers produced a relatively anterior /ɲ/, differing minimally from /n/ followed by /j/. Whereas one of their QF peers produced uniquely backed velar realizations of /ɲ/, the other speaker had fronted alveolopalatal variants word-medially versus backed velar realizations word-finally, with the latter differing minimally from the /ŋ/ of jogging. These findings are consistent with pathways to depalatalization observed in other Romance varieties and call into question the phonemic status of the palatal nasal in French.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".