Epigenetics and the Environment in Bioethics
Bibliographic record
Abstract
A rich literature in public health has demonstrated that health is strongly influenced by a host of environmental factors that can vary according to social, economic, geographic, cultural or physical contexts. Bioethicists should, we argue, recognize this and--where appropriate--work to integrate environmental concerns into their field of study and their ethical deliberations. In this article, we present an argument grounded in scientific research at the molecular level that will be familiar to--and so hopefully more persuasive for--the biomedically-inclined in the bioethics community. Specifically, we argue that the relatively new field of molecular epigenetics provides novel information that should serve as additional justification for expanding the scope of bioethics to include environmental and public health concerns. We begin by presenting two distinct visions of bioethics: the individualistic and rights-oriented and the communitarian and responsibility-oriented. We follow with a description of biochemical characteristics distinguishing epigenetics from genetics, in order to emphasize the very close relationship that exists between the environment and gene expression. This then leads to a discussion of the importance of the environment in determining individual and population health, which, we argue, should shift bioethics towards a Potterian view that promotes a communitarian-based sense of responsibility for the environment, in order to fully account for justice considerations and improve public health.
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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 | 0.039 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.100 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| 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".