Bilingualism, Aging, and Cognitive Control
Bibliographic record
Abstract
Research in cognitive aging has advanced enormously in the past few decades, producing detailed studies and sophisticated models of agerelated changes in cognitive functions (see chapters in Craik & Salthouse, 2000 ). Most of this research involves English-speaking participants, and conclusions have been drawn with little or no regard to the possibility that the participants might also speak another language. Yet the existing evidence strongly suggests that bilingualism has an effect on cognitive processing, at least for children and younger adults (see chapters in de Groot & Kroll, 1997 , and Harris, 1992 ). What has not been examined is whether these effects persist over the life span and continue to influence changes in cognitive processing in bilingual older adults. One current reality is that bilingualism is increasingly common in many countries. As an example, the 1996 Canadian Census reported that approximately 11% of Canadians spoke English or French at home in addition to some other language; when only respondents more than age 65 were considered, the figure was 13% ( Canada Census 1996 , n.d.). In the United States, 17.9% of Americans reported that they spoke a language other than English at home, and it is a reasonable assumption that most of them also speak English ( U.S. Census Bureau, 2003 ). Given the prevalence of bilingualism in North American society (and the prevalence is certainly greater in most European countries), it is important to establish the precise effects of bilingualism on cognitive processing and the way in which these effects are modulated by aging.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".