Bibliographic record
Abstract
Recent findings in epigenetics have been attracting much attention from social scientists and bioethicists because they reveal the molecular mechanisms by which exposure to socioenvironmental factors, such as pollutants and social adversity, can influence the expression of genes throughout life. Most surprisingly, some epigenetic modifications may also be heritable via germ cells across generations. Epigenetics may be the missing molecular evidence of the importance of using preventive strategies at the policy level to reduce the incidence and prevalence of common diseases. But while this "policy translation" of epigenetics introduces new arguments in favor of public health strategies and policy-making, a more "clinical translation" of epigenetics is also emerging. It focuses on the biochemical mechanisms and epigenetic variants at the origin of disease, leading to novel biomedical means of assessing epigenetic susceptibility and reversing detrimental epigenetic variants. In this paper, we argue that the impetus to create new biomedical interventions to manipulate and reverse epigenetic variants is likely to garner more attention than effective social and public health interventions and therefore also to garner a greater share of limited public resources. This is likely to happen because of the current biopolitical context in which scientific findings are translated. This contemporary neoliberal "regime of truth," to use a term from Michel Foucault, greatly influences the ways in which knowledge is being interpreted and implemented. Building on sociologist Thomas Lemke's Foucauldian "analytics of biopolitics" and on literature from the field of science and technology studies, we present two sociological trends that may impede the policy translation of epigenetics: molecularization and biomedicalization. These trends, we argue, are likely to favor the clinical translation of epigenetics-in other words, the development of new clinical tools fostering what has been called "personalized" or "precision" medicine. In addition, we argue that an overemphasized clinical translation of epigenetics may further reinforce this biopolitical landscape through four processes closely related to neoliberal pathways of thinking: the internalization and isolation (aspects of liberal individualism) of socioenvironmental determinants of health and increased opportunities for commodification and technologicalization (aspects of economic liberalism) of health care interventions.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.091 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".