How is Chinese English Learners’ L2 Metaphoric Competence Related to That of L1? An E-Prime-Based Multi-Dimensional Study
Bibliographic record
Abstract
Even though transfer from L1 to L2 has been repeatedly tested and confirmed, there is little literature and consensus on how and to what extent the L1 metaphoric competence could be related to that of L2. Based on the metaphor acceptability and response time of E-Prime experiments and two written tests of comprehension and production of metaphors on 94 intermediate Chinese-speaking university students of English, this study compares Chinese English learners’ similarities and differences in four dimensions (metaphor acceptability, identification speed, metaphor comprehension, and metaphor production) of metaphoric competence between L1 and L2 (here is Chinese and English). The results demonstrate that: Chinese English learners’ L1 metaphoric competence is significantly better than that of L2; their L2 metaphoric competence is significantly correlated to that of L1, and the regression analysis shows that L1 metaphoric competence has a significant prediction of that of L2. These findings enlighten us to greatly cultivate metaphoric competence in foreign language teaching and help students create connection between L1 and L2 metaphoric competence. This study also provides statistical support for the claim that metaphoric competence is a general trans-language cognitive ability for Chinese English learners.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".