Beyond policy analysis: the raw politics behind opposition to healthy public policy
Bibliographic record
Abstract
Despite evidence that public policy that equitably distributes the prerequisites/social determinants of health (PrH/SDH) is a worthy goal, progress in achieving such healthy public policy (HPP) has been uneven. This has especially been the case in nations where the business sector dominates the making of public policy. In response, various models of the policy process have been developed to create what Kickbusch calls a health political science to correct this situation. In this article I examine an aspect of health political science that is frequently neglected: the raw politics of power and influence. Using Canada as an example, I argue that aspects of HPP related to the distribution of key PrH/SDH are embedded within issues of power, influence, and competing interests such that key sectors of society oppose and are successful in blocking such HPP. By identifying these opponents and understanding why and how they block HPP, these barriers can be surmounted. These efforts to identify opponents of HPP that provide an equitable distribution of the PrH/SDH will be especially necessary where a nation's political economy is dominated by the business and corporate sector.
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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 | 0.053 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.064 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".