Language Dominance and Cognitive Flexibility in French–English Bilingual Children
Bibliographic record
Abstract
Some studies have reported a cognitive advantage for bilingual children over monolinguals and other studies have not. One possible reason for these conflicting results is that the degree of cognitive flexibility is related to individual differences in language dominance and use. More balanced bilinguals who separate their languages by context might have to learn to reduce inter-language interference and therefore show greater cognitive flexibility. The goal of the present study was to test if language dominance is related to French-English bilingual children's cognitive flexibility, using three different measures of language dominance: (1) parental reports of dominance, (2) relative scores on vocabulary tests, and (3) knowledge of translation equivalents. We also included two measures of language use: (1) living in a bilingual community (Montreal) or a monolingual community (Edmonton) and (2) language separation. Sixty-two French-English bilingual between 46 and 85 months of age participated. Children's cognitive flexibility was assessed using the Advanced Dimensional Change Card Sort task. Children's language knowledge and use was assessed in both French and English using a battery of tests. The results showed that none of the measures of language dominance or language use predicted cognitive flexibility. These results are inconsistent with the claim that individual differences in language dominance and use predict bilinguals' executive function s.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".