Bibliographic record
Abstract
A generous welfare state decommodifies social relations and frees citizens from relying excessively on markets. We argue that decommodification is associated with population health in two ways: directly, as it provides better social protection to households and indirectly, as it mitigates health-damaging labour market polarization and reduces the incidence of labour market risks. Using time-series cross-sectional quantitative analysis for 21 OECD countries from 1971 to 2010, we observe a negative relationship between decommodification and the age-standardized death rate. We then analyze three correlates of decommodification-income redistribution, labour market polarization and the reduction of labour market risk incidence-and find that only the latter two are associated with population health. Higher labour market polarization, measured by the share of market income allocated to the richest decile relative to the share of the poorest decile, is associated with a higher death rate. A new measure of risk reduction, the degree to which the welfare state reduces the prevalence of large income losses, is also associated with lower death rates, especially for men. Welfare state decommodification thus contributes to population health directly, and indirectly, via the attenuation of labour market polarization and the mitigation of labour market risks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".