Gradient behavior without gradient underlying representations: the case of French liaison
Bibliographic record
Abstract
French liaison consonants are challenging for phonological theory because they pattern ambiguously between word-initial and word-final consonants. In recent works, these facts have been used to motivate different underlying representations for liaison consonants and non-liaison consonants. This paper argues that this move is not necessary. The gradient behavior of liaison consonants can indeed be derived through constraint interaction while maintaining that liaison consonants and non-liaison consonants have the same underlying representation. Two independently motivated hypotheses will play a key role in deriving this result: (i) word variants strive to be similar to their citation forms via output-output correspondence and (ii) concatenating two words (word 1 and word 2) has phonetic/phonological consequences on word 1's final segment and on word 2's initial segment. Together with the fact that liaison consonants are absent from the citation forms of liaison words, these hypotheses predict that liaison consonants will be less protected against changes than stable word-final consonants but more protected than word-initial consonants, thus explaining their gradient behavior. The analysis is illustrated with a detailed case study on Quebec French affrication combining corpus data and grammatical modeling.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".