Investigating the citing communities around three leading health-system frameworks
Bibliographic record
Abstract
Of numerous proposed frameworks for analyzing and impacting health systems, three stand out for the large number of publications that cite them and for their links to influential international institutions: Murray and Frenk (Bull World Health Organ 78:717-31, 2000) connected initially to the World Health Organization (WHO) and then to the Global Burden of Disease Project; Roberts et al. (Getting health reform right: a guide to improving performance and equity, Oxford University Press, Oxford, 2004) sponsored by the World Bank/Harvard Flagship Program; and de Savigny and Adam (Systems thinking for health systems strengthening, WHO, 2009) linked to the WHO and the Alliance for Health Policy and Systems Research. In this paper, we examine the citation communities that form around these works to better understand the underlying logic of these citation grouping as well as the dynamics of Global Health research on health systems. We conclude that these groupings are largely independent of one another, reflecting a range of factors including the goals of each framework and the problems that it was meant to explore, the prestige and authority of institutions and individuals associated with these frameworks, and the intellectual and geographic proximity of the citing researchers to each other and to the framework authors.
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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 | 0.075 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.059 | 0.075 |
| Science and technology studies | 0.044 | 0.028 |
| Scholarly communication | 0.038 | 0.022 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".