Prosodic focus in English vs. French: A scope account
Bibliographic record
Abstract
We compare the use of prosodic prominence in English and French to convey focus. While previous studies have found these languages, and Germanic vs. Romance more generally, to differ in their use of prominence to encode focus (e.g., Ladd 1990; 1996; 2008; Lambrecht 1994; Cruttenden 1997; 2006), exactly what underlies the difference remains an open question. We investigate two possibilities: The difference between the languages could be due to a difference in their phonology, restricting the circumstances in which material can be prosodically reduced, as proposed in Féry (2014). Alternatively, there could be syntactic, semantic, and/or pragmatic differences concerning when prominence can be used to encode focus. We compare these hypotheses in a production study which varied the type of focus context (corrective, contrastive, parallelism) to establish the contextual conditions on when a shift in prosodic prominence can occur. The results confirm earlier claims that French uses prosodic prominence to encode focus in corrections, but fails to prosodically encode other types of focus, in contrast to English. We further find that French and English encode focus with very similar acoustic means. Our results show that both languages have the phonological/phonetic means to encode focus using prominence shifts, but differ with respect to the semantic and pragmatic circumstances in which they use them. We propose that these semantic/pragmatic differences between English and French are a result of differences in the syntactic scope possibilities of the focus operator involved in prosodic focus marking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".