Epigenomic Stories: Evidence of Harm and the Social Justice Promises and Perils of Environmental Epigenetics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it