Bibliographic record
Abstract
For well over a century, the politics of universal health care have shaped the development of modern welfare states and their ability to manage economic inequality.Whether governments adopt universal health care in response to workers' struggles, capitalist labor demand, or other factors, universal health care tends to advance economic redistribution.1 This equity effect of universal health care is often overlooked, including in the human rights field. 2 Although right to health standards are clear on states' obligation to finance health care equitably, along with providing universal access to quality health facilities, goods, and services, the distributional impact of a universal system has received less consideration.3 I propose that right to health advocates embrace universal health care as a redistributive project that can help advance not only the right to health but also economic equality.Both are deeply intertwined.The United States presents a prime example.It is one of the most unequal wealthy countries, where resources are concentrated in the hands of a few while millions struggle to access basic economic and social rights.The top 10% of US households own approximately 70% of the total wealth, and the typical white family is about ten times wealthier than the typical Black family.4 Life expectancy and health outcomes are below average, compared to other OECD countries, yet health expenditure is the highest.5 Despite spending twice as much per capita on health as Canada or France, the pre-COVID-19 mortality rate from treatable causes was over a third higher in the United States than in Canada and twice as high as in France.6 While poor and unequal health outcomes point to health system failures, economic and social structures are key underlying factors.The pandemic brought this into sharp focus: COVID-19 mortality has been positively associated with country-level income inequality.7 The United States has among the highest COVID-19 mortality rate in the world, disproportionately affecting Black, Indigenous, and low-income populations.8 Economic and social inequalities drive much of this unconscionable toll on human lives.A large body of research confirms that societies with greater income inequality have poorer health outcomes.9 But if economic inequality is at the root of poor health outcomes, does the health care system matter?It does.Inequalities are maintained and reproduced by the systems and institutions that organize
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".