Bibliographic record
Abstract
Résumé Cet article fait la synthèse des travaux sur le vieillissement et les variations stratégiques. En particulier, il décrit à quelles conditions la perspective stratégique nous permet de faire des progrès dans notre compréhension du vieillissement cognitif et des mécanismes sous-jacents à l’évolution des performances cognitives avec l’âge. Ainsi, nous proposons d’examiner comment un cadre conceptuel faisant la distinction entre le répertoire, la distribution, l’exécution et la sélection stratégiques est pertinent pour, notamment, réconcilier les données contradictoires de la littérature. Par ailleurs, l’utilisation de méthodes appropriées (comme la méthode choix/non choix) permet d’évaluer indépendamment et sans biais les aspects stratégiques des performances cognitives et leur évolution avec l’âge. Nous illustrons ces intérêts avec certains de nos résultats obtenus dans le domaine de la résolution de problèmes arithmétiques et discutons comment ces résultats se généralisent aisément à d’autres domaines de la cognition humaine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".