Word Writing vs. Meaning Inferencing in Contextualized L2 Vocabulary Learning: Assessing the Effect of Different Vocabulary Learning Strategies
Bibliographic record
Abstract
The majority of L2 vocabulary studies concentrate on learning word meaning and provide learners with opportunities for semantic elaboration (i.e., focus on word meaning). However, in initial vocabulary learning, engaging in structural elaboration (i.e., focus on word form) with a view to acquiring L2 word form is equally important. The present contextual word-learning study aims to compare the effects of an increased attention to form condition and an increased attention to meaning condition. Native speakers of Dutch (N = 50) learned new English vocabulary in a meaning-inferencing condition, which focused their attention on word meaning, and a word-writing condition, which prompted the learners to focus on word form. The results demonstrate that the word-writing condition advanced both form recall and meaning recall to a greater extent than the meaning-inferencing condition. We conclude that word writing benefits initial word learning more than meaning inferencing in a contextual word-learning situation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".