Estimating second language productive vocabulary size
Bibliographic record
Abstract
This study provides validity evidence for the Capture-Recapture (CR) method, borrowed from ecology, as a measure of second language (L2) productive vocabulary size (PVS). Two separate “captures” of productive vocabulary were taken using written word association tasks (WAT). At Time 1, 47 bilinguals provided at least 4 associates to each of 30 high-frequency stimulus words in English, their first language (L1), and in French, their L2. A few days later (Time 2), this procedure was repeated with a different set of stimulus words in each language. Since the WAT was used, both Lex30 and CR PVS scores were calculated in each language. Participants also completed an animacy judgment task assessing the speed and efficiency of lexical access. Results indicated that, in both languages, CR and Lex30 scores were significantly positively correlated (evidence of convergent validity). CR scores were also significantly larger in the L1, and correlated significantly with the speed of lexical access in the L2 (evidence of construct validity). These results point to the validity of the technique for estimating relative L2 PVS. However, CR scores are not a direct indication of absolute vocabulary size. A discussion of the method’s underlying assumptions and their implications for interpretation are provided.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".