Is L2 Exposure Always a Strong Modulator of L1 Influence? Evidence from Chinese EFL Learners Acquiring English Collocations
Bibliographic record
Abstract
Despite the voluminous body of research investigating the role of L1 influence in acquiring L2 collocations, research that examines the extent to which L2 exposure modulates L1 influence is relatively scant. The present study, therefore, aims to address this under-studied issue. To this end, two types of collocations comprising congruent collocations (i.e., collocations which have literal translation equivalents in learners’ L1) and non-congruent collocations (i.e., collocations that do not have L1 literal translation) were used as materials to elicit the potential role of L1 in acquiring L2 collocations. A blank-filling collocation test and an acceptability judgement collocation test were designed and then administered to three groups of Chinese EFL learners differing in L2 exposure, i.e., years of instructions (freshmen, sophomores and juniors), affording the chance to explore the possible relationship between L1 influence and L2 exposure. The findings indicate that (a) L1 had a robust and persistent impact on acquiring L2 collocations at both reception and production level, regardless of the amount of L2 exposure received; (b) with an increase in L2 exposure, receptive/productive congruent and non-congruent collocational knowledge was developed in parallel (from freshman to sophomore), and then plateaued or even decreased (from sophomore to junior), suggesting that L2 exposure may not always be a strong modulator of L1 influence. Possible reasons and pedagogical implications arising from this study are discussed.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".