Fixed-choice word-association tasks as second-language lexical tests: What native-speaker performance reveals about their potential weaknesses
Bibliographic record
Abstract
ABSTRACT Qian and Schedl's Depth of Vocabulary Knowledge Test was administered to 31 native-speaker undergraduates under an “unconstrained” condition, in which the number of responses to headwords was unfixed, whereas a corresponding group ( n = 36) completed the test under the original “constrained” condition. Results revealed lower accuracy in the unconstrained condition and in paradigmatic versus syntagmatic responses. Native speakers failed to reach the 90% criterion on most unconstrained and many constrained items. Although certain modifications could improve such a test (e.g., eliminating psycholinguistically anomalous headwords, such as adjectives, or presenting responses to headwords discontinuously), two intransigent problems impede test validity. First, collocates in the mental lexicon differ in tightness and vary across dialects, sociolects, and age groups. Second, it is more serious that second-language Depth of Vocabulary Knowledge Tests are likely spot checks of metalinguistic knowledge rather than depth tests that reflect what learners would actually produce in spontaneous utterances.
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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 | 0.004 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".