Metalinguistic Ability in Bilingual Children: The Role of Executive Control.
Bibliographic record
Abstract
Although bilingual children tend to obtain lower scores than their monolingual peers on tests of formal language ability, they exhibit a processing advantage on non-verbal executive control (EC) tasks. This advantage may be attributable to EC practice that bilinguals routinely receive from the constant need to manage attention to two jointly activated languages. Metalinguistic tasks, unlike linguistic tasks, require children to access both their language knowledge (i.e., representations) and recruit EC ability; that is, metalinguistic tasks require children to use attentional processes to operate on linguistic forms. In this article, we review our recent studies examining linguistic and metalinguistic abilities in tasks that differed in the extent to which solutions were based on linguistic knowledge (representations) or control processes, allowing us to examine the relative contribution of each to bilingual language processing. Results indicate that bilinguals' superior EC ability allows them to compensate for weaker linguistic knowledge in metalinguistic tasks where greater recruitment of control processes is required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".