Using big data to understand bilingual performance in semantic fluency: Findings from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: This study aimed to characterize verbal fluency performance in monolinguals and bilinguals using data from the Canadian Longitudinal Study on Aging (CLSA). METHODS: A large sample of adults aged 45-85 (n = 12,875) completed a one-minute animal fluency task in English. Participants were English-speaking monolinguals (n = 9,759), bilinguals who spoke English as their first language (L1 bilinguals, n = 1,836), and bilinguals who spoke English as their second language (L2 bilinguals, n = 1,280). Using a distributional modeling approach to quantify the semantic similarity of words, we examined the impact of word frequency and pairwise semantic similarity on performance on this task. RESULTS: Overall, L1 bilinguals outperformed monolinguals on the verbal fluency task: they produced more items, and these items were of lower average frequency and semantic similarity. Monolinguals in turn outperformed L2 bilinguals on these measures. The results held across different age groups, educational, and income levels. DISCUSSION: These results demonstrate an advantage for bilinguals compared to monolinguals on a category fluency task, when performed in the first language, indicating that, at least in the CLSA sample, bilinguals have superior semantic search capabilities in their first language compared to monolingual speakers of that language.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".