The Role of Depth versus Breadth of Vocabulary Knowledge in Success and Ease in L2 Lexical Inferencing
Bibliographic record
Abstract
This study determines whether breadth and depth of vocabulary knowledge are related to L2 ease and success in lexical inferencing. To this end, two tests meas- uring vocabulary breadth and depth were administered to 50 participants. Two weeks later, all participants received an inferencing task and rated the degree of perceived ease in inferencing on a 6-point Likert-scale questionnaire. The findings indicated that although both vocabulary breadth and depth played an important role in lexical inferencing success, vocabulary breadth made a more important contribution. The results further revealed that neither vocabulary breadth nor depth had a significant effect on perceived ease of inferencing.Cette étude détermine dans quelle mesure l’étendue et la profondeur des connais- sances lexicales sont liées à la facilité en L2 et à la réussite en inférence linguis- tique. À cette fin, nous avons fait passer à cinquante participants deux examens pour évaluer l’étendue et la profondeur de leurs connaissances lexicales. Deux semaines plus tard, nous avons donné à tous les participants une tâche d’inférence et en avons évalué le degré de facilité perçue avec un questionnaire en 6 points sur l’échelle Likert. Les résultats indiquent que si l’étendue et la profondeur des connaissances lexicales jouent tous les deux un rôle important dans la réussite en inférence lexicale, l’étendue du vocabulaire y contribuent davantage. L’étude a également démontré que ni l’étendue ni la profondeur des connaissances lexi- cales n’ont un effet significatif sur la facilité d’inférence perçue.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".