Les conseillers en politiques du secteur public devraient-ils se soucier de la théorie des optima de second rang ?
Bibliographic record
Abstract
La théorie des optima de second rang est une contribution formelle au domaine de l’optimisation de l’utilité (ou du bien-être). Elle stipule que, dans certaines circonstances «imparfaites », l’approximation d’un idéal est sous-optimale. Dans cet article, nous tentons de déterminer si la théorie peut être utile aux conseillers oeuvrant dans le secteur public. Nous présentons les conditions dans lesquelles la théorie pourrait s’avérer pertinente pour les conseillers du secteur public, et en particulier la condition d’inséparabilité entre différentes variables d’un problème de politiques publiques. Nous tentons aussi de déterminer si ces conditions sont réunies dans différents documents produits par les conseillers en politiques publiques. Pour ce faire, nous analysons 40 publications récentes du secteur public québécois comprenant des recommandations.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".