The Contribution of Ethnography to Epigenomics Research: Toward a New Bio-Ethnography for Addressing Health Disparities
Bibliographic record
Abstract
This article describes ethnography as a research method and outlines how it excels in capturing the salient experiences of individuals among diverse communities in their own words. We argue that the integration of ethnographic findings into epigenomics will significantly improve disparities-focused study designs within environmental epigenomics by identifying and contextualizing the most salient dimensions of the 'environment' that are affecting local communities. Reciprocally, epigenetic findings can enhance anthropological understanding of human biological variation and embodiment. We introduce the term bio-ethnography to refer to research designs that integrate both of these methodologies into a single research project. Emphasis is given in this article, through the use of case studies, to socially disadvantaged communities that are often under-represented in scientific literature. The paper concludes with preliminary recommendations for how ethnographic methods can be integrated into epigenomics research designs in order to elucidate the manner in which disadvantage translates into disparities in the burden of illness.
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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 | 0.050 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".