From international health to global health: how to foster a better dialogue between empirical and normative disciplines
Bibliographic record
Abstract
BACKGROUND: Public health recommendations are usually based on a mixture of empirical evidence and normative arguments: to argue that authorities ought to implement an intervention that has proven effective in improving people's health requires a normative position confirming that the authorities are responsible for improving people's health. While public health (at the national level) is based on a widely accepted normative starting point - namely, that it is the responsibility of the state to improve people's health - there is no widely accepted normative starting point for international health or global health. As global health recommendations may vary depending on the normative starting point one uses, global health research requires a better dialogue between researchers who are trained in empirical disciplines and researchers who are trained in normative disciplines. DISCUSSION: Global health researchers with a background in empirical disciplines seem reluctant to clarify the normative starting point they use, perhaps because normative statements cannot be derived directly from empirical evidence, or because there is a wide gap between present policies and the normative starting point they personally support. Global health researchers with a background in normative disciplines usually do not present their work in ways that help their colleagues with a background in empirical disciplines to distinguish between what is merely personal opinion and professional opinion based on rigorous normative research. If global health researchers with a background in empirical disciplines clarified their normative starting point, their recommendations would become more useful for their colleagues with a background in normative disciplines. If global health researchers who focus on normative issues used adapted qualitative research guidelines to present their results, their findings would be more useful for their colleagues with a background in empirical disciplines. Although a single common paradigm for all scientific disciplines that contribute to global health research may not be possible or desirable, global health researchers with a background in empirical disciplines and global health researchers with a background in normative disciplines could present their 'truths' in ways that would improve dialogue. This paper calls for an exchange of views between global health researchers and editors of medical journals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".