Income inequality and population health: a political-economic research agenda
Bibliographic record
Abstract
There is more than 30 years of research on relationships between income inequality and population health. In this article, we propose a research agenda with five recommendations for future research to refine existing knowledge and examine new questions. First, we recommend that future research prioritise analyses with broader time horizons, exploring multiple temporal aspects of the relationship. Second, we recommend expanding research on the effect of public expenditures on the inequality-health relationship. Third, we introduce a new area of inquiry focused on interactions between social mobility, income inequality and population health. Fourth, we argue the need to examine new perspectives on 21st century capitalism, specifically the population health impacts of inequality in income from capital (especially housing), in contrast to inequality in income from labour. Finally, we propose that this research broaden beyond all-cause mortality, to cause-specific mortality, avoidable mortality and subcategories thereof. We believe that such a research agenda is important for policy to respond to the changes following the COVID-19 pandemic.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".