The Human Genome Organisation: towards next-generation ethics
Bibliographic record
Abstract
Ten years after the completion of the human genome [1], looking back over the policy statements of the Human Genome Organisation's (HUGO) Ethics Committee (EC) and of its Intellectual Property Committee (IPC) is more than just a trip down memory lane; it is the revelation of a seismic shift in the values underlying genomic research (Table 1).Founded in 1992 at the inception of the Human Genome Project, HUGO not only provided prospective scientifi c leadership on approaches to intellectual property, but also on ethical, legal and social issues.Indeed, the Statements constitute a harbinger of policy debates that persist today.In the past two decades, genetic research ethics has expanded rapidly from a domain seemingly 'ungoverned by any explicitly ethical or legal norms' , to a rich and sophisticated fi eld [2].Initially, the concerns of the EC and IPC were to ensure participant autonomy through informed consent, respect for participant privacy and confi dentiality in light of the sensitive nature of genetic information, and an equitable distribution of the burdens and benefi ts of genetic research.Th ese concerns are prevalent throughout the HUGO Statements, even as the focus of genetic ethics has shifted from the protection of individuals, families and communities, to considering the broader interests of society, and international harmonization.
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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 | 0.082 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".