Four Decades of Health Economics through a Bibliometric Lens
Bibliographic record
Abstract
This paper takes a bibliometric tour of the past 40 years of health economics using bibliographic "metadata" from EconLit supplemented by citation data from Google Scholar and the authors' topical classifications. The authors report the growth of health economics (33,000 publications since 1969 -- 12,000 more than in the economics of education) and list the 300 most-cited publications broken down by topic. They report the changing topical and geographic focus of health economics (the topics 'Determinants of health and ill-health' and 'Health statistics and econometrics' both show an upward trend, and the field has expanded appreciably into the developing world). They also compare authors, countries, institutions, and journals in terms of the volume of publications and their influence as measured through various citation-based indices (Grossman, the US, Harvard and the JHE emerge close to or at the top on a variety of measures).
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.052 | 0.083 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".