Performance on the Boston Naming Test in Bilinguals
Bibliographic record
Abstract
OBJECTIVES: We examined performance on the Boston Naming Test (BNT) in older and younger adults who were monolingual English or French speakers, or bilingual speakers of English and French (n=215). METHODS: Monolingual participants completed the task in their native language, and bilingual participants completed the task in English, French, and bilingual (either-language) administrations. RESULTS: Overall, younger and older monolingual French speakers performed worse than other groups; bilingual participants performed worst in the French administration and approximately two-thirds of bilingual participants performed better when responses were accepted in either language. Surprisingly, however, a subset of bilinguals performed worse when responses were accepted in either language as compared to their maximum score achieved in either English or French. This either-language disadvantage does not appear to be associated with the degree of balanced bilingualism, but instead appears to be related to overall naming abilities. Differential item analysis comparing language groups and the different administrations identified several items that displayed uniform and/or non-uniform differential item functioning (DIF). CONCLUSIONS: The BNT does not elicit equivalent performance in English and French, even when assessing naming performance in monolingual French speakers using the French version of the test. Scores were lower in French overall, and several items exhibited DIF. We recommend caution in interpreting performance on these items in bilingual speakers. Finally, not all bilinguals benefit from an either-language administration of the BNT.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".