Language-specific cognitive flexibility is related to code-switching habits and interactional context; domain-general cognitive flexibility is not
Bibliographic record
Abstract
Individualistic experience with code-switching has often been found to modulate bilingual executive functioning, though the direction of these effects is variable. The present study investigated whether French-English code-switching in a primarily dual-language context (the environment which requires the most control processes) may lead to increased cognitive flexibility. Sustained (mixing) and transient (switching) cognitive flexibility was examined in a domain-general task and a novel language-specific task (i.e. a cued bilingual lexical decision task). First, mixing and switching effects in the domain-general task were not reliably predicted by the participants’ code-switching habits. Second, though the sample displayed minimal switching effects and a mixing benefit in the language-specific task, these were positively predicted by the participants’ deliberate code-switching. By contrast, predictors related to dense code-switching were negatively related to the participants’ language-specific sustained cognitive flexibility. Altogether, our findings indicate that any training instilled by dual-language code-switching is restricted to language-specific cognitive flexibility.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".