Epigenomic Stories: Evidence of Harm and the Social Justice Promises and Perils of Environmental Epigenetics
Bibliographic record
Abstract
This article develops the concept of epigenomic stories to analyze how scientists describe and study the relationships between environmental epigenetics, health inequities, and social justice. Based on a multisited ethnography of epigenetic knowledge production and its circulation across laboratories, clinics, and communities in the United States and Canada between 2016 and 2021, we build on Black feminist and science studies scholarship to convey the racial, gender, and epistemic consequences of epigenomic stories. We argue that these stories reflect how scientists position epigenetics as a way of providing biological evidence of social harms and shifting responsibilities from individuals to broader structures. Yet these stories also reflect the limits of epigenetic methods and models in effectively capturing and addressing lived experiences of oppression. Thus, while scientists envision epigenetics as a resource for social change, they do so in ways that privilege biological ways of knowing. In analyzing the values and power relations embedded in these practices, we argue that epigenomic stories reflect what is at stake socially, politically, and materially when we tell stories with science. We contend that efforts to mobilize epigenetic knowledge for social justice must therefore center marginalized peoples’ knowledge and experiences and address how racism and sexism shape science and its social consequences.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".