Bibliographic record
Abstract
This book is about creating conversations and interdisciplinary dialogue between epidemiology and medical anthropology. In recent decades, an increasing number of public health institutions have appealed to specialists of the “cultural factor” to help them enhance their understanding of risk factors. At the same time, many medical anthropologists recognize that the two disciplines share an interest in how disease and risks factors vary in time and space among different populations, as well as within subpopulations of a single country. In this book, Trostle analyzes the origins of an integrated approach in anthropology and epidemiology, as well as its conceptual and methodological bases. One chapter is about unpacking variables and the assumptions underlying disease-pattern categories, such as person, place, and time. Other chapters address the cultural issues involved in measuring disease prevalence and anthropology's contribution to the design of disease prevention and health promotion programs. It includes many examples of projects integrating anthropology and epidemiology (such as the study of cholera in Latin America), which illustrate the potential for such integrated perspectives. The book closes with an analysis of lay and professional ideas about risk and the difficulties inherent in communicating about risk.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".