Depth versus Breadth of Lexical Repertoire: Assessing Their Roles in EFL Students’ Incidental Vocabulary Acquisition
Bibliographic record
Abstract
This study explores the roles of depth and breadth of lexical repertoire in L2 lexical inferencing success and incidental vocabulary acquisition through reading. Students read a graded reader containing 13 pseudo-words and attempted to infer the meanings of underlined target words. The Word Associates Test (WAT, Read, 2004) and the Vocabulary Levels Test (Schmitt, Schmitt, & Clapham, 2001) were administered to measure depth and breadth of lexical repertoire respectively. To rate retention of inferred meanings, I administered the Vocabulary Knowledge Scale (VKS, Paribakht & Wesche, 1996, 1997) with a repeated measure design. The results indicated that (a) both breadth and depth of lexical knowledge correlated positively with long-term retention of inferred word meanings. However,depth of vocabulary knowledge indicated a higher correlation; and (b) scores on both breadth and depth of vocabulary knowledge had a significant positive correlation with success of lexical inferencing through reading, but depth of vocabularyknowledge was a stronger predictor of inferencing success.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".