How well are primary and secondary meanings of L2 words acquired?
Bibliographic record
Abstract
Abstract Most words in a language have more than one meaning. Yet, few studies have explicitly examined the acquisition of secondary meanings of L2 words and the extent to which polysemy and homonymy affect vocabulary learning. This study explores the effect of polysemy and homonymy on the deliberate acquisition of the form–meaning connections of L2 words. Thirty-six EFL learners (compared with a control group of 30) learned secondary polysemous and homonymous meanings of familiar words and primary meanings of unfamiliar words using flashcards. Knowledge of target words was measured using meaning–recall and meaning–recognition tests immediately after the treatment and again one week later. The findings indicated that learning another meaning for a familiar word was just as difficult as learning the primary meaning of an unfamiliar word, suggesting that the type of meaning (primary, secondary polysemous, or secondary homonymous) might not be an influencing factor in the deliberate acquisition of L2 words.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".