The View from the Hogan: Cultural Epidemiology and the Return to Ethnography
Bibliographic record
Abstract
Alexander Leighton's seminal work has clearly demonstrated how ethnographic experience provides the rich cultural context in which epidemiological data are best interpreted. This article reviews recent trends in cultural epidemiology, and especially the emergence of the EMIC (Explanatory Model Interview Catalogue) as a quantitatively oriented tool designed to assess culture. It is suggested that such efforts do not reflect more recent trends in culture theory, and tend to view 'cultures' as easily bounded and largely homogenous units to facilitate the generation of quantitative data. It is argued that cultural epidemiologists should take a step back and ask, 'what is the culture in question here?' and 'how do I know if it is appropriate to place any given member of my sample into a specific cultural category?' before proceeding with any 'culturally appropriate' instrument. The answer to these questions begins with a return to ethnography as a means to elucidate and describe culture within the context in which it is being presented and studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.010 | 0.116 |
| Scholarly communication | 0.017 | 0.039 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".