Bibliographic record
Abstract
The influence of association measures has been little examined in research on L2 collocation processing. For this reason, the present study replicated Öksüz et al. (2020) experiment on intermediate L2 learners of English to determine whether the association measure mutual information (MI) is a stronger predictor of L2 performance than the Log Dice measure. Twenty-two intermediate Arab learners of English completed a timed acceptability judgment task on the online Gorilla platform. The task included (1) high-frequent collocations (e.g., bad news), (2) low-frequent collocations (e.g., only friend), and (3) non-collocates (e.g., true news, wrong friend) which had differing MI and Log Dice scores. Mixed-effects models were built to analyze the participants’ reaction times to the three conditions. The results showed that the frequency of the collocation (operationalized as item type) and its length significantly influenced reaction times, while both MI and Log Dice scores did not surface as significant predictors. This suggests that intermediate English L2 learners are not sensitive to corpus-based association measures. The results have important implications for L2 teaching and testing and may indicate that it is not worthwhile to determine which collocations to include in the materials based mainly on the strength of the association.
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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 | 0.007 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".