Breadth and depth of English vocabulary knowledge : which really matters in the academic reading performance of Chinese university students?
Bibliographic record
Abstract
This study explored the relationship between vocabulary size (i.e., breadth of knowledge), depth of vocabulary knowledge, and reading comprehension of Chinese-speaking ESL (English as a second language) university students in Canada. Both aspects of vocabulary knowledge, breadth and depth, continue to play roles in vocabulary research. Few studies, however, have focused on which aspect plays the predominant role in L2 reading. Using three language tests---the GRE (Graduate Record Examinations) for reading comprehension, Nation's (1990) Vocabulary Levels Test, and Read's (1998) Word Associates Test---and verbal reports, the general purpose of the study was to examine the relationship between vocabulary knowledge and reading comprehension, and the specific focus was to find out which aspect of vocabulary knowledge, breadth or depth, has greater impact on determining reading comprehension performance. The results demonstrate that (1) test scores on vocabulary size, depth of vocabulary knowledge, and reading comprehension are positively correlated, (2) vocabulary size is a stronger predictor of reading comprehension than depth of vocabulary knowledge, and (3) breadth and depth of vocabulary knowledge are closely interrelated and mutually facilitative. The findings suggest the importance of vocabulary size in reading comprehension for the population tested.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".