The 15-item version of the Boston Naming Test as an index of English proficiency
Bibliographic record
Abstract
Objective: The present study was designed to examine the potential of the Boston Naming Test – Short Form (BNT-15) to provide an objective estimate of English proficiency. A secondary goal was to examine the effect of limited English proficiency (LEP) on neuropsychological test performance.Method: A brief battery of neuropsychological tests was administered to 79 bilingual participants (40.5% male, MAge = 26.9, MEducation = 14.2). The majority (n = 56) were English dominant (EN), and the rest were Arabic dominant (AR). The BNT-15 was further reduced to 10 items that best discriminated between EN and AR (BNT-10). Participants were divided into low, intermediate, and high English proficiency subsamples based on BNT-10 scores (≤6, 7–8, and ≥9). Performance across groups was compared on neuropsychological tests with high and low verbal mediation.Results: The BNT-15 and BNT-10 respectively correctly identified 89 and 90% of EN and AR participants. Level of English proficiency had a large effect (partial η2 = .12–.34; Cohen’s d = .67–1.59) on tests with high verbal mediation (animal fluency, sentence comprehension, word reading), but no effect on tests with low verbal mediation (auditory consonant trigrams, clock drawing, digit-symbol substitution).Conclusions: The BNT-15 and BNT-10 can function as indices of English proficiency and predict the deleterious effect of LEP on neuropsychological tests with high verbal mediation. Interpreting low scores on such measures as evidence of impairment in examinees with LEP would likely overestimate deficits.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".