Epigenetics, ethics, law and society: A multidisciplinary review of descriptive, instrumental, dialectical and reflexive analyses
Bibliographic record
Abstract
Epigenetics, defined as 'the study of mitotically and/or meiotically heritable changes in gene function that cannot be explained by changes in DNA sequence', has emerged as a promissory yet controversial field of scientific inquiry over the past decade. Scholars from many disciplines have formulated both optimistic and cautionary claims regarding its potential normative implications. This article provides a comprehensive review of the nascent literature at the crossroads of epigenetics, ethics, law and society. It describes nine emerging areas of discussion, relating to (1) the impact of epigenetics on the nature versus nurture dualism, (2) the potential resulting biologization of the social, (3) the meaning of epigenetics for public health, its potential influence on (4) reproduction and parenting, (5) political theory and (6) legal proceedings, and concerns regarding (7) stigmatization and discrimination, (8) privacy protection and (9) knowledge translation. While there is some degree of similarity between the nature and content of these areas and the abundant literature on ethical, legal and social issues in genetics, the potential implications of epigenetics ought not be conflated with the latter. Critical studies on epigenetics are emerging within a separate space of bioethical and biopolitical investigations and claims, with scholars from various epistemological standpoints utilizing distinct yet complementary analytical approaches.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Science and technology studies Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".