L’impact des transferts publics et des taxes sur la pauvreté au Canada et aux États-Unis
Bibliographic record
Abstract
Dans cet article, nous analysons l’impact des politiques de transferts publics sur la pauvreté au Canada et aux États-Unis à l’aide de la base de données du Luxembourg Income Study . Pour ce faire, nous utilisons une méthode basée sur la valeur de Shapley afin de pouvoir attribuer à chaque politique un impact sur la pauvreté qui est indépendant de l’ordre arbitraire dans laquelle on aurait pu la considérer. Nous constatons que la pauvreté est plus élevée aux États-Unis qu’au Canada. Ceci est principalement dû au fait que les politiques de transferts au Canada sont plus généreuses qu’aux États-Unis. Nous montrons aussi que la principale source de réduction de la pauvreté dans les deux pays provient des transferts vers les personnes âgées.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".