Principles and methods of global legal epidemiology
Bibliographic record
Abstract
Although the theory and methods of legal epidemiology-the scientific study and deployment of law as a factor in the cause, distribution, and prevention of disease and injury in a population-have been well developed in the context of domestic law, the challenges posed by shifting the frame of analysis to the global legal space have not yet been fully explored. While legal epidemiology rests on the foundational principles that law acts as an intervention, that law can be an object of scientific study and that law has impacts that should be evaluated, its application to the global level requires the recognition that international laws, policies and norms can cause effects independently from their legal implementation within countries. The global legal space blurs distinctions between 'hard' and 'soft' law, often operating through pathways of global agenda setting, legal language, political pressures, social mobilisation and trade pressures to have direct impacts on people, places and products. Despite these complexities, international law has been overwhelmingly studied as operating solely through national policy change, with only one global quasi-experimental evaluation of an international law's impact on health published to date. To promote greater adoption of global legal epidemiology, we expand on an existing typology of public health law studies with examples of policymaking, mapping, implementation, intervention and mechanism studies. Global legal epidemiology holds great promise as a way to produce rigorous and impactful research on the international laws, policies and norms that shape our collective health, equity and well-being.
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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 | 0.136 | 0.179 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".