From Neighborhoods to Molecules: The Selective Appropriation of Sociology in Social Epigenetics
Bibliographic record
Abstract
Social epigenetics is presented as a promising interdisciplinary avenue between the natural and social sciences to explore the links between neighborhood environments, epigenetic modifications and health outcomes. Sociological concepts and methods are mobilized, sometimes through direct collaboration between epidemiologists and sociologists, to grasp the embodiment of social inequalities. Drawing on an in-depth qualitative analysis of three epidemiological cohort studies in the United States, we offer a processual approach to the use of Chicago-style ecological research on disorganization in social epigenetic studies. We argue that a selective appropriation of sociological research operates at two different levels of study design: that of the overall cohort study in which social epigenetics research is conducted and data are obtained, and that of the social epigenetics studies. This selective appropriation represents one of the most successful attempts in social epigenetics to complexify concepts and methods for analyzing health outcomes as a product of social situations. We show that for the epidemiologists and sociologists working in this interdisciplinary space, the move to greater complexity means observing the social organization of neighborhoods from a rather narrow window of observation, but one that presumably allows them to produce robust statistical evidence for the biological embodiment of social conditions.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.088 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".