Bibliographic record
Abstract
An important proposal in the insightful Keynote Article by Baum and Titone is that the field of bilingualism research needs to attend more closely to intersubject variability in order to understand the nature of neuroplastic changes in the brains of bilingual speakers as they age. I agree. Understanding such variability and its drivers (such as the contexts of language use that Baum and Titone nicely comment on in the Montreal milieu) will help us develop theoretical accounts of the cognitive control processes recruited in bilingual speakers and establish how adaptive changes to these processes mediate the effects of normal aging; yield protective effects against neurodegenerative disease, such as Alzheimer disease; and modulate language recovery poststroke in bilingual speakers. In this commentary I explore some aspects of this variability and commend, in line with the views expressed in the Keynote Article, the value of relating behavioral indices to whole brain structural magnetic resonance imaging for enriching our understanding of experience-dependent changes. Allied to tractography studies, such research can help us develop a rich picture of the major drivers of neuroplastic changes.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".