A Reanalysis of “The Town with No Poverty: The Health Effects of a Canadian Guaranteed Annual Income Field Experiment”
Bibliographic record
Abstract
Dans « The Town with No Poverty: The Health Effects of a Canadian Guaranteed Annual Income Field Experiment » (publié dans Canadian Public Policy/Analyse de politiques en 2011), la professeure Evelyn Forget analyse les effets sur la santé communautaire et l’éducation de l’expérience MINCOME, réalisée au Manitoba dans les années 1970, qui portait sur le revenu annuel minimum garanti. Réévaluant les données sur les visites à l’hôpital utilisées dans Forget (2011), je soutiens que les données relatives de la communauté traitée de Dauphin présentent, avant l’intervention, des tendances négatives statistiquement significatives par rapport aux communautés de contrôle. La tendance préexistante, combinée à des périodes de préanalyse et de traitement courtes, remet sérieusement en question l’utilité de ces données qui, dans la mesure où elles sont réellement utilisables, montreraient plutôt les effets positifs de l’expérience MINCOME sur les visites à l’hôpital dans un contexte normalisé. J’en conclus que les données de la MINCOME ne soutiennent pas l’affirmation qu’un revenu annuel minimum garanti réduit le cout des soins de santé.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".