The Need to Draw Second Language Learners' Attention to the Semantic Boundaries of Syntactically Relevant Verb Classes
Bibliographic record
Abstract
This paper puts forward the view that second language (L2) learners could benefit from being made aware of the semantic components which unify verbs that display a certain syntactic behaviour, and, more significantly, the semantic components which exclude other verbs from participating in this behaviour. In other words, an awareness of the parameters of verb classes could minimize both syntactic overgeneralization and under-generalization. This viewpoint is supported by the findings of a study in which production and judgement data on the behaviour of 'change-of-state' and 'directional motion' verbs were elicited from learners at three levels of lexical proficiency. While learners with high lexical proficiency had fewer overgeneralizations than their lower-level counterparts, overgeneralization was substantial at all levels of proficiency in the directional motion class. The results suggest that it might be beneficial to combine grammar and vocabulary instruction in L2 curricula, with a special focus on semantically coherent verb classes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".