Cross-linguistic Interpretation of Figurative Expressions in Collaborative Language Learning: Gaps as Instances for Learning
Bibliographic record
Abstract
The aim of this article is to explore how students approach, try to understand, and interpret figurative expressions in the context of language learning. Interpreting figurative language represents a challenge as languages are rich in metaphorical expressions and contextual/local references. The empirical study was carried out in the context of collaborative work in classrooms with students 12 to 13 years of age, translating two songs, one in English and one in Swedish. The results show that the students struggle to capture the sense of the lyrics. Addressing these difficulties, students engage in language-related episodes (LRE’s) where they topicalize language, meta-communicate and inquire into acceptable cross-linguistic interpretations that preserve the metaphorical nature of the expressions. Their linguistic sensitivity and familiarity with specific metaphorical expressions, contextual and local references are challenged, and their learning process involves an increasing capacity to talk about and reflect on the meaning potentials of linguistic expressions.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".