Desperately seeking reductions in health inequalities in Canada: Polemics and anger mobilization as the way forward?
Bibliographic record
Abstract
Progress in reducing health inequalities through public policy action is difficult in nations identified as liberal welfare states. In Canada, as elsewhere, researchers and advocates provide governing authorities with empirical findings on the sources of health inequalities and document the lived experiences of those encountering these adverse health outcomes with the hope of provoking public policy action. However, critical analysis of the societal structures and processes that make improving the sources of health inequalities difficult-the quality and distribution of living and working conditions, that is the social determinants of health-identifies limitations in these approaches. Within this latter critical tradition, we consider-using household food insecurity in Canada as an illustration-how polemics and anger mobilization, usually absent in health inequalities research and advocacy-could force Canadian governing authorities to reduce health inequalities through public policy action. We explore the potential of using high valence terms such as structural violence, social death and social murder, which make explicit the adverse outcomes of health-threatening public policy to force government action. We conclude by outlining the potential benefits and threats posed by polemics and anger mobilization as means of promoting health equity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".