Testing the benefits of relating figurative idioms to their literal underpinnings
Bibliographic record
Abstract
Abstract Second/foreign language (L2) learners appear to remember figurative idioms relatively well if they are informed of the literal underpinning of the expressions, that is, the context in which the expressions were (or still are) used in a literal sense. In the present exploratory study, ESL learners read texts accompanied by glosses which did or did not mention the literal underpinnings of idioms used in the texts, and their recollection of the idioms was tested immediately after the reading task and again one week later. The mean test scores were very similar across the gloss conditions, suggesting no mnemonic benefits of giving literal underpinnings. However, retrospective interviews with the participants revealed considerable variation in the way they had engaged with the materials. For example, several students who were not given information about the literal underpinnings speculated about those underpinnings spontaneously, while those who were given this information did not always understand its relation to the idiomatic meanings. The interviews also revealed considerable variation in the students’ perception of the purpose of the glosses, with some treating them as support for text comprehension and others treating them as input for deliberate vocabulary study. The findings illustrate how mixed-methods research that looks not just at aggregated learning outcomes but at individuals’ learning processes can help to finetune expectations about the efficacy of an instructional intervention and, ultimately, perhaps help to optimize the intervention itself.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".