The BNT-15 provides an accurate measure of English proficiency in cognitively intact bilinguals – a study in cross-cultural assessment
Bibliographic record
Abstract
This study was designed to replicate earlier reports of the utility of the Boston Naming Test - Short Form (BNT-15) as an index of limited English proficiency (LEP). Twenty-eight English-Arabic bilingual student volunteers were administered the BNT-15 as part of a brief battery of cognitive tests. The majority (23) were women, and half had LEP. Mean age was 21.1 years. The BNT-15 was an excellent psychometric marker of LEP status (area under the curve: .990-.995). Participants with LEP underperformed on several cognitive measures (verbal comprehension, visuomotor processing speed, single word reading, and performance validity tests). Although no participant with LEP failed the accuracy cutoff on the Word Choice Test, 35.7% of them failed the time cutoff. Overall, LEP was associated with an increased risk of failing performance validity tests. Previously published BNT-15 validity cutoffs had unacceptably low specificity (.33-.52) among participants with LEP. The BNT-15 has the potential to serve as a quick and effective objective measure of LEP. Students with LEP may need academic accommodations to compensate for slower test completion time. Likewise, LEP status should be considered for exemption from failing performance validity tests to protect against false positive errors.
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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 | 0.006 |
| 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 | 0.000 | 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".