Bibliographic record
Abstract
Adam Wagstaff (2009) reports on a statistical comparison of social health insurance (sHI) versus tax financed (Tf) health systems within the OECD.On average, sHI financing is more expensive than Tf and yields no better health outcomes.It lowers overall labour force participation and reduces the share of the formal sector.Why, then, is interest in sHI increasing in developing countries?Consider the historical origins for sHI and Tf.Bismarck (sHI) was a Prussian aristocrat; Beveridge (Tf) was a socialist.Tf is inherently egalitarian; sHI adapts readily to the preservation of inequality and privilege in both financing and access to care.This may be the real attraction of sHI in countries with highly unequal income distributions. RésuméAdam Wagstaff (2009) fait part d'une comparaison statistique entre, d'une part, le système d' assurance maladie sociale (AMS) et, d' autre part, le système de services de santé financés par les fonds publics (SFP), dans les pays de l'OCDE.En moyenne, le financement de l' AMS est plus coûteux que celui des SFP et ne mène pas à de meilleurs résultats en matière de santé.L' AMS réduit la participation globale de la maind' œuvre et diminue la part du secteur structuré.Pourquoi, donc, les pays en développement s'y intéressent-ils de plus en plus?
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".