Public health‐care provision in the Canadian provinces and American states
Bibliographic record
Abstract
Abstract: While there are broad differences between the health‐care systems in Canada and the United States, sub‐national variation is significant in both countries (though more notably among American states), both in terms of the role of government in the provision of health‐care as well as outputs of the health‐care system such as access and cost‐control. Although this variation is rarely considered in comparisons of health‐care policy in the two countries, the value of using the United States as a comparative reference in contemporary policy debates in Canada would be considerably greater if such variations were utilized to augment the analytical leverage resulting from such comparisons. Sommaire: Alors que les différences entre les systèmes de soins de santé au Canada et aux États‐Unis sont importantes, l'écart infranational est considérable dans les deux pays (bien que plus notoire parmi les États américains) aussi bien en ce qui concerne le rôle joué par le gouvemement dans la prestation des soins de santé, que les extrants du système de soins de santé comme l'accès et le contrôle des coûts. Même si cet écart est rarement pris en compte dans les comparaisons de la politique de soins de santé des deux pays, le fait de recourir aux États‐Unis comme point de référence comparatif dans les débats aduels sur la politique au Canada aurait beaucoup plus de valeur si un tel écart était utilisé pour accroître le levier analytique résultant de telles comparaisons.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".