Bibliographic record
Abstract
Phrasal verbs present persistent challenges for second language (L2) learners due to their figurative meanings, multiple interpretations, and variable word order. This article reviews five strands of research that contribute to effective phrasal verb instruction: corpus-based studies, which identify what to teach, and four instructional approaches—input-based, output-based, retrieval-based, and cognitive linguistic—that offer insights into how to teach. Corpus studies identify high-frequency phrasal verbs, common meanings, and register variation, while learner corpus research reveals patterns of underuse and cross-linguistic influence. Instructional studies show that textual enhancement promotes noticing, and output and retrieval tasks improve retention when carefully sequenced. Cognitive linguistic (CL) approaches foster generalization through conceptual metaphors and particle meanings, though their success depends on learner readiness. Despite these advances, most strands have been examined in isolation. This review calls for research on integrated instructional sequences that align corpus-informed selection, CL scaffolding, and retrieval practice. It also highlights the need to investigate how retrieval and communicative output can be coordinated to support durable, flexible phrasal verb use in L2 learning. Les verbes à particule posent des problèmes persistants aux apprenants d’une langue seconde (L2) en raison de leurs sens figurés, de leurs interprétations multiples et de l’ordre variable des mots. Cet article passe en revue cinq axes de recherche qui contribuent à un enseignement efficace des verbes à particule : les études basées sur des corpus, qui identifient ce qu’il faut enseigner, et quatre approches pédagogiques — basées sur l’intrant, basées sur la production, basées sur la récupération et la linguistique cognitive — qui offrent des éclairages sur la manière d’enseigner ces verbes. Les études de corpus identifient les verbes à particule très fréquents, les sens fréquents et les variations de registre, tandis que les recherches sur les corpus d’apprenants révèlent des tendances de sous-utilisation et d’influence translinguistique. Les études pédagogiques montrent que la mise en évidence du texte favorise la prise de conscience, et que les tâches de production et de récupération améliorent la rétention lorsqu’elles sont soigneusement ordonnées. Les approches de linguistique cognitive (LC) favorisent la généralisation par le biais de métaphores conceptuelles et des sens des particules, bien que leur succès dépende du degré de préparation de l’apprenant. Malgré ces avancées, la plupart des axes de recherche ont été examinés de manière isolée. Ce texte appelle à des recherches sur des séquences d’enseignement intégrées qui alignent la sélection basée sur le corpus, l’échafaudage offert par la LC et la pratique de récupération. Il souligne également la nécessité d’étudier la manière dont la récupération et la production communicative peuvent être coordonnées pour soutenir l’utilisation durable et flexible des verbes à particule dans l’apprentissage d’une L2.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".