Reducing Social and Health Inequalities Requires Building Social and Political Movements
Bibliographic record
Abstract
Health inequalities are an outcome of social inequalities and both result from the workings of the economic system, a governmental apparatus that maintains or reinforces these inequalities, and a public discourse that justifies these inequalities. The outcome of these processes is a skewed distribution of exposures among the population to various social (societal) determinants of health. Modifying these societal processes—thereby improving the social determinants of health—requires developing and implementing public policies consistent with reducing these inequalities. Two viewpoints dominate discussions of how this might be brought about: a) professionally-oriented rational or knowledge-based approaches and b) social and political movement-based materialist or political economy-oriented approaches. In political economies dominated by business interests such as those seen in Canada, the US, and UK, adopting a social and political movement-based approach is the most appropriate avenue of action. How this might be accomplished requires critical analysis of the political, economic, and social forces that lead jurisdictions to implement policies that either support or resist equity-oriented public policy innovations.
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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 | 0.032 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".