Bibliographic record
Abstract
Thousands of human genes, many associated with human disease processes and diagnosis, have been patented in Canada. The scope of these patents has restricted public access to genetic testing and raised the question of whether human genetic material should be subject to differential treatment by our patent law regime. The Canadian Intellectual Property Office (CIPO) has failed to offer guidelines on the application of patent laws to genetic material, symptomatic of the broader problem of a lack of strong federal leadership in this area. In this paper I will engage the debate over patenting of human genes specifically as it relates to disease gene patents and will critically discuss various proposals for reform. For the purpose of my discussion I have assumed that access to genetic testing (specifically for breast cancer susceptibility) is desirable, that restricting access to testing is not ethically justifiable and that commodification of human genes can be harmful. Re-establishing an appropriate balance between private and public interests in biotechnology requires patent reform. In arguing for patent reform, I will focus on Myriad Genetics, a company that holds patent rights to breast cancer susceptibility genes [discussed infra] and is attempting to establish a worldwide monopoly on breast cancer susceptibility testing. Myriad's claims have begun to stir a debate in the public over the application of patent law to the human genome and the potential harms of permitting commercial monopolies over genetic testing services.
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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 | 0.037 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.040 | 0.020 |
| 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".