Ambiguity related to French liaisons: The role of bottom-up and top-down processes
Bibliographic record
Abstract
French liaison is a phonological process involving the surfacing of an underlying floating consonant as the onset of a vowel-initial word when the word is preceded by a liaison-causing word. We used vowel- and consonant-initial ambiguous target words following four liaison-related contexts /z/, /n/, /t/, /r/ (e.g., ces onches - ces zonches). Targets included nouns and pseudo-nouns. Quebec-French-speaking adults performed a production task in which they produced the last word upon hearing each phrase. Their productions thus reflected their interpretation of the onset of the liaison-ambiguous words. One bottom-up (acoustic cues) and three top-down (noun token frequency, word onset probability, contextual liaison knowledge) factors were investigated. Acoustic cues did not guide listeners' interpretation of the word forms, since they often produced the targets incorrectly. Moreover, vowel- and consonant-initial ambiguous targets largely yielded the same pattern of responses. Among the top-down factors, contextual liaison knowledge had a dominant influence. In particular, participants gave vowel-initial responses to both vowel-initial and consonant-initial ambiguous targets when the liaison frequency was high (e.g., "onches" responses for both "ces onches" and "ces zonches"), and they gave targets consonant-initial responses when the liaison frequency was low. These results demonstrate the importance of contextual knowledge in lexical recognition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".