Une idée d'une étonnante simplicité? Les freins à la mise en œuvre d'une allocation universelle
Bibliographic record
Abstract
Résumé Cet article envisage la mise en œuvre d'une allocation universelle, un sujet peu traité dans les travaux de recherches menés sur le revenu minimum. Nous relevons et étudions trois freins majeurs susceptibles d'empêcher un régime d'allocation universelle d'atteindre la portée universelle souhaitée et prônée par ses partisans: a) gérer le registre de tous les bénéficiaires potentiels au sein de la population afin de toucher un nombre maximal de personnes, b) définir des modalités de paiement rigoureuses qui puissent s'appliquer à tous les bénéficiaires, et c) prévoir un dispositif de supervision dans un contexte politique qui s'oppose au contrôle des assurés. Nous estimons que la mise en œuvre d'une allocation universelle soulève des questions particulières que les défenseurs de ce système doivent étudier de manière approfondie.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".