L2 Learner Production and Processing of Collocation: A Multi-study Perspective
Bibliographic record
Abstract
Abstract: This article presents a series of studies focusing on L2 production and processing of adjective–noun collocations (e.g., social services). In Study 1, 810 adjective–noun collocations were extracted from 31 essays written by Russian learners of English. About half of these collocations appeared frequently in the British National Corpus (BNC); one-quarter failed to appear in the BNC at all, while another quarter had a very low BNC frequency. Based on frequency data and mutual information (MI) scores, it was discovered that around 45% of all learner collocations were, in fact, appropriate collocations, that is, frequent and strongly associated English word combinations. When the study data were compared to data from native speakers, very little difference was found between native speakers (NS) and non-native speakers (NNS) in the use of appropriate collocations. Unfortunately, the high percentage of appropriate collocations does not mean that NNSs necessarily develop fully native-like knowledge of collocation. In Study 2, NNSs demonstrated poorer intuition than NS respondents regarding the frequency of collocations. Likewise, Study 3 showed that NNSs were slower than NSs in processing collocations. Overall, the studies reported here suggest that L2 learners are capable of producing a large number of appropriate collocations but that the underlying intuitions and the fluency with collocations of even advanced learners do not seem to match those of native speakers.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".