Using economics to advance health equity: What we know, don't know and need to know (but may never know) from Markus Haacker
Bibliographic record
Abstract
This paper on using economics to advance health equity synthesizes selected works of Markus Haacker, who has passed away recently, on the impact of HIV and HIV spending on inequality and inequity. Addressing these issues was a major driving force of Haacker's career, from his first assignment at the International Monetary Fund (IMF) in 1998, over his 2016 book “The Economics of the Global Response to HIV/AIDS”, to his most recent work on macroeconomic analysis for health policy evaluation. Showing how Haacker brought a “soul” to his study of the AIDS pandemic – a feature frequently lacking in mainstream economic analyses, this paper is an appraisal of Haacker's work, clearly laying out his empirical, theoretical and methodological contributions, and offers insights and bold ideas on thinking about and acting on inequality and inequity in the context of the rapidly changing HIV epidemic and response, and the financing of the same in the era of polycrisis. It also presents the ensuing implications for policy and practice.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| 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".