Genetic testing, ethical concerns, and the role of patent law
Bibliographic record
Abstract
This article examines the changing debate over gene patenting and the possible connection between patent law and the ethical and policy concerns associated with the use of genetic testing technologies (e.g. the premature implementation and inappropriate marketing of genetic tests). Arguably, patent law helps to form the market forces that lead to these concerns. It is suggested that existing safeguards fail to control these concerns because of, for example, a lack of provider knowledge and an absence of an adequate regulatory framework. While patent law can be associated with a number of ethical and policy concerns, the article also suggests that patent law may have a positive role in reducing them. Patent law provides policy makers and the public with a focal point - the patent holder - upon which to attach accountability for ethical and legal conduct. The article concludes by inviting policy makers to consider the ways in which patent law could be modified in order to optimize its constructive influence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.093 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.032 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".