Inferring the Meaning of Idioms: Does Accuracy Matter for Retention in Memory?
Bibliographic record
Abstract
There are grounds for believing that prompting language learners to infer the meaning of new lexical items is beneficial because inferring the meaning of lexical items and verifying one's inferences invites more cognitive investment than simply being presented with the meanings. However, concerns have been raised over the risk that wrong inferences interfere with later recall of the correct meanings. The present study examines the effect of inferencing on language learners’ retention of idiomatic expressions (e.g. jump the gun, pull your weight and stay the course). In a counter-balanced within-participant design, 26 advanced learners of English were presented with 21 idioms in contexts either with their meaning clarified from the start ( k = 7) or with the instruction to try and infer their meaning before receiving the clarification. The latter condition was designed so that accurate interpretations were more likely for some idioms ( k = 7) than for others ( k = 7). The learners’ responses at the inferencing stage were collected for analysis. One week later, the participants took an unannounced meaning-recall test. Recall was the most successful in the learning condition where the likelihood of accurate inferences was high. Items that had been inferred accurately stood a better chance (odds ratio 1.22) of being recalled than items whose interpretation had needed to be rectified. Approximately 13% of the wrong or imperfect inferences re-emerged in the post-test, suggesting that the learners did not readily discard them despite the corrective feedback. The findings indicate that, for inferencing procedures to be optimally useful, they need to be implemented in ways that ensure a high success rate.
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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 | 0.011 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".