Explicit and implicit semantic processing of verb–particle constructions by French–English bilinguals
Bibliographic record
Abstract
Verb–particle constructions are a notoriously difficult aspect of English to acquire for second-language (L2) learners. The present study investigated whether L2 English speakers are sensitive to gradations in semantic transparency of verb–particle constructions (e.g.,finish upvs.chew out). French–English bilingual participants (first language: French, second language: English) completed an off-line similarity ratings survey, as well as an on-line masked priming task. Results of the survey showed that bilinguals’ similarity ratings became more native-like as their English proficiency levels increased. Results from the masked priming task showed that response latencies from high, but not low-proficiency bilinguals were similar to those of monolinguals, with mid- and high-similarity verb–particle/verb pairs (e.g.,finish up/finish) producing greater priming than low-similarity pairs (e.g.,chew out/chew). Taken together, the results suggest that L2 English speakers develop both explicit and implicit understanding of the semantic properties of verb–particle constructions, which approximates the sensitivity of native speakers as English proficiency increases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".