CONTEXTUAL WORD LEARNING IN THE FIRST AND SECOND LANGUAGE
Bibliographic record
Abstract
Abstract Access to definitions facilitates the learning of word meanings when novel words are encountered in reading. However, the memorial costs and benefits of inferring word meanings from context, compared to seeing definitions of unfamiliar words before reading, are not yet well understood. We conducted two experiments with adult L1 (English) and L2 (Chinese) readers to investigate whether the development of declarative and nondeclarative word knowledge benefits more when definitions are supplied before reading (errorless treatment) or after reading (trial-and-error treatment). Study participants encountered 90 target vocabulary items three times in short informative texts under errorless or trial-and-error conditions and entered their meaning inferences immediately after reading each text. Posttreatment, we evaluated participants’ declarative knowledge of the target items using a meaning generation (recall) task and nondeclarative knowledge using a self-paced reading task. The trial-and-error treatment followed by definitions resulted in a superior declarative and nondeclarative knowledge, compared to the errorless treatment, for L1 and L2 readers. Inference errors affected the development of declarative but not nondeclarative knowledge, and the trajectory of the development of nondeclarative knowledge was different for L1 and L2 readers. We interpret these findings in terms of the declarative and nondeclarative memory processes underpinning contextual word learning.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".