Mind the Gap: Towards Determining Which Collocations to Teach
Bibliographic record
Abstract
Collocations form part of formulaic language use that is considered by many scholars as central to communication (Henriksen 2013; Wray 2002). Today, most scholars agree that teaching collocations to second and/or foreign language users (henceforth “L2 students”) is a must. This study offers a reflection on the directions L2 researchers and teachers may explore, and that could contribute to modelling the teaching of collocations or at least spark the debate on this issue. The fundamental point raised here is the extent to which pedagogy may be informed by knowing the most common lexical collocations (combinations of content words) and using frequency of collocates as a key factor in selecting which collocations to bring to learners’ attention. The results from this study indicate that out of the eight different lexical collocations, adjective+noun and verb+noun collocations are the most common, and should therefore be introduced first. Furthermore, most collocates (“co-occurring words” in Sinclair’s (1991) terms) come from the 1,000 and 2,000 most frequent words. Therefore, this study suggests that the same way that “[u]sing the computational approach as a starting point makes it possible to distinguish between collocations of varying frequency of use” (Henriksen 2013: 32), frequency may be used to select the target words and their collocates once collocations have been identified. This could potentially contribute to addressing the issue of selection criteria of which collocations to teach.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".