Bibliographic record
Abstract
We are all living in the era of globalization, and like it or not, it is going to change the way we practice epidemiology, the kinds of questions we ask, and the methods we use to answer them. Increasingly, pubic health problems are being shifted from rich countries to poor countries and from rich to poor populations within Western countries. There is increasing interest and concern about the situation in non-Western populations on the part of Western epidemiologists, with regards to collaborative research, skills transfer, and 'volunteerism' to enable the 'benefits' of Western approaches to epidemiology to be shared by the non-Western world. However, most existing collaborations benefit Western epidemiologists rather than the countries in which the research is conducted. Even when research in non-Western populations is conducted as a genuine collaboration, it can too often 'export failure' from the West. On the other hand, non-Western epidemiologists are increasingly developing new and innovative approaches to health research that are more appropriate to the global public health issues they are addressing. These include recognition of the importance of context and the importance of diversity and local knowledge, and a problem-based approach to addressing the major public health problems using appropriate technology. These debates formed the background for a plenary session on 'International Epidemiology and International Health' at the recent International Epidemiological Association (IEA) meeting in Montreal, and the papers from this session are presented here. The development of a truly global epidemiology can not only better address the public health problems in non-Western populations, but can shed light on the current limitations of epidemiology in addressing the major public health problems in the West.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".