Comment les expériences sociales avec assignation aléatoire permettent de mieux comprendre le comportement et les politiques de bien-être1
Bibliographic record
Abstract
Cet article explique de quelle façon des expériences sociales à grande échelle faisant appel à une assignation aléatoire, comme le Projet d’autosuffisance du Canada, permettent d’aborder d’importantes questions sociologiques et développementales. Nous expliquons d’abord comment la répartition aléatoire résout le problème de biais qui se retrouve dans la plupart des recherches fondées sur des enquêtes. Nous passons ensuite en revue les méthodes et résultats de deux séries d’expériences récentes faisant appel à une répartition au hasard, l’une qui manipulait la situation économique des familles et l’autre qui manipulait les conditions du quartier. En conclusion, nous analysons les forces et faiblesses de cette approche expérimentale.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".