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Record W4415622157 · doi:10.1080/21622965.2025.2576692

The Boston naming test is a valid psychometric marker of English proficiency in bilingual Cubans

2025· article· en· W4415622157 on OpenAlexaff
Klency González Hernández, Sarah Schneider, Gabriela Diago-Monzón, Sabrina Prado Verdecia, Iulia Crișan, Brenda Cabrera‐Mendoza, Daniela Escobar Magarino, László A. Erdődi

Bibliographic record

VenueApplied Neuropsychology Child · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTest (biology)Neuroscience of multilingualismVariance (accounting)Language proficiencyBoston Naming TestPsychometricsIndex (typography)Validation testLanguage assessment

Abstract

fetched live from OpenAlex

OBJECTIVE: This study was designed to evaluate the utility of the short form of the Boston Naming Test (BNT-15) as an index of English proficiency and its relationship with the Common European Framework of Reference for Languages (CEFR). METHOD: The BNT-15 was administered to a sample of 51 Cuban university students (native speakers of Spanish) with various levels of English proficiency as part of a battery of neuropsychological tests administered in both Spanish and English. RESULTS: The BNT-15 and CEFR were positively correlated with each other and relative language proficiency. A linear relationship emerged between levels of English proficiency operationalized using BNT-15 scores or CEFR ratings for tests with high verbal mediation. However, English proficiency was unrelated to performance on tests with low verbal mediation. Item-level responses on the BNT-15 suggest culture- and language-specific influences independent of overall level of English proficiency. CONCLUSIONS: The BNT-15 and CEFR provide valid measures of English proficiency, although both leave a high percentage of variance in test performance unexplained. Large-scale replications are needed to further explore the utility of the BNT-15 as an index of English proficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.295
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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