The Human Genome Diversity Project: The Politics of Patents at the Intersection of Race, Religion, and Research Ethics
Bibliographic record
Abstract
The patenting of human genetic materials provokes wide‐ranging misgivings about the appropriate place and scope of intellectual property protections. The issues implicated range from anti‐competitive practices in the market, the imposition of limits on biomedical research, increasing costs for health care, research ethics, potentials for racial discrimination, and various violations of human rights. Exploring controversies around the Human Genome Diversity Project, patents on genetic sequences, and patents on higher life forms such as the so‐called “Harvard mouse,” the authors find that North American patent policy has developed in the absence of necessary political debate. They link this de‐politicization to the hegemony of neo‐liberal principles most fully demonstrated by the incorporation of intellectual property under international trade negotiations. They point, however, to the recent emergence and increasing audibility of new social movements that seek to reposition issues of intellectual property in larger debates about human rights, distributional equalities, and social justice.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.025 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".