Le vieillissement des populations comme variable causale à effets connus : comment éviter des conclusions hâtives
Bibliographic record
Abstract
En sciences sociales on a, de manière parfois expéditive, attribué au vieillissement des populations le rôle de variable clé pour l’explication des changements économiques et sociaux qui surviennent dans nos sociétés. Cet article porte sur les outils utilisés par les chercheurs et sur la manière dont ils pourraient les exploiter pour éviter de tirer des conclusions hâtives dans leurs travaux. À l’aide d’une typologie, les auteurs cataloguent et critiquent les méthodes qui servent à mesurer l’impact du vieillissement démographique. Adoptant une position théorique et épistémologique, ils ne souhaitent pas clore le débat, mais plutôt l’amorcer.
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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 | 0.046 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".