Human rights in the postgenomic era: Challenges and opportunities arising with epigenetics
Bibliographic record
Abstract
Over the past twenty-five years, international organizations have adopted human rights declarations in an attempt to address emerging ethical, legal and social concerns associated with genetic research and technologies. While these declarations point to important challenges and potential issues in genetics, the focus on genetics has been criticized for promoting the idea that there is something unique about our genes, and that therefore, they deserve special protections in our laws. It is also argued that this ‘genetic exceptionalism’ perspective has contributed to a reinvigoration of genetic essentialism and determinism. In this article, we add to this criticism by pointing out gaps and flaws in current gene-focused human rights declarations in light of recent developments in the field of epigenetics. First, we show that these documents do not provide guidance for a responsible governance of epigenetic data (e.g., privacy protection) and an ethical use of individual epigenetic information (e.g., nondiscrimination). This is particularly concerning given the interest recently demonstrated by insurance companies, forensic scientists and immigration agencies in using epigenetic clock technologies. Second, we argue that findings in epigenetics could contribute to the promotion of second- and third- generation human rights, i.e., respectively, economic, social and cultural rights, and solidarity rights. We conclude by calling for international bioethics and human rights organizations to pay greater attention to epigenetics and other postgenomic sciences in the coming years.
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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 | 0.036 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.075 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".