Bibliographic record
Abstract
The aim of this article is to present and summarize the main research findings inthe area of learning and teaching second language (L2) collocations. Being a largepart of naturally occurring language, collocations and other types of multiwordunits (e.g., idioms, phrasal verbs, lexical bundles) have been identified as importantaspects of L2 proficiency that need to be promoted through language instruction.However, while in recent years the field of applied linguistics has witnessedan impressive rise in the number of studies exploring the process of learning andusing L2 collocations, there is still little consensus as to the most effective waysof enhancing this kind of knowledge. The aim of this article is to review the literaturein this area, highlight the main findings pertaining to teaching English as asecond (ESL) and foreign (EFL) learners, and point to future research directions.L’objectif de cet article est de présenter et résumer les résultats principaux derecherche dans le domaine de l’apprentissage et l’enseignement des expressions figéesen L2. Constituant une partie importante d’une langue naturelle, les expressionsfigées et d’autres types d’unités composées (p. ex. expressions idiomatiques,verbes à particule) sont des aspects importants de la compétence en L2 que l’enseignementde la langue doit promouvoir. Toutefois, si le nombre d’études portantsur l’apprentissage et l’emploi des expressions figées en L2 a augmenté de façonimportante dans le domaine de la linguistique appliquée récemment, un faibleconsensus existe quant aux moyens qui sont les plus efficaces pour favoriser cesconnaissances. L’objectif de cet article est d’examiner la littérature de ce domaine,souligner les résultats principaux relatifs à l’enseignement de l’anglais langueseconde et l’anglais langue étrangère, et indiquer des pistes de recherches futures.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".