Lexical attrition in younger and older bilingual adults
Bibliographic record
Abstract
Healthy monolingual older adults experience changes in their lexical abilities. Bilingual individuals immersed in an environment in which their second language is dominant experience lexical changes, or attrition, in their first language. Changes in lexical skills in the first language of older individuals who are bilinguals, therefore, can be attributed to the typical processes accompanying older age, the typical processes accompanying first-language attrition in bilingual contexts, or both. The challenge, then, in understanding how lexical skills change in bilingual older individuals, lies in dissociating these processes. This paper addresses the difficulty of teasing apart the effects of ageing and attrition in older bilinguals and proposes some solutions. It presents preliminary results from a study of lexical processing in bilingual younger and older individuals. Processing differences were found for the older bilingual participants in their first language (L1), but not in their second language (L2). It is concluded that the lexical behaviour found for older bilinguals in this study can be attributed to L1 attrition and not to processes of ageing. These findings are discussed in the context of previous reports concerning changes in lexical skills associated with typical ageing and those associated with bilingual L1 attrition.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".