High ceilings and ingenuine allies: tapping into the idiom meaning knowledge of first and second language speakers of English
Bibliographic record
Abstract
Abstract Idioms are undoubtedly important for second language (L2) learners, who encounter them in instructed learning, textbooks/resources and in out-of-class language use. While research on first language (L1) and L2 idiom comprehension shows how well L1/L2 speakers understand various idioms and the role of different predictors, important questions remain about how knowledge varies with more difficult task types and stimuli, how well L1 ‘norms’ serve L2 learners, how subjective and objective predictors of idiom knowledge interact and how L2 learner inferencing works in learning idioms. To address these issues, university-age L1 and L2 English (L1 German) participants provided meaning descriptions and familiarity ratings for 100 challenging idioms from learner resources, and each idiom was assigned an OpenAI-generated transparency rating, corpus-based frequency and to one of six cross-language overlap (CLO) types. Descriptive statistics showed lower and more varied idiom meaning knowledge than might be expected, especially for the L1ers, who were some way off ceiling level. Mixed-effects regression revealed familiarity and transparency as positive L1 and L2 knowledge predictors, but groups differed in sensitivity to idiom frequency, which only mattered for the L1ers and CLO, which (as expected) only mattered for the L2ers, who mistook false friends as genuine allies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".