MétaCan
Menu
Back to cohort
Record W7073551630

Biomedical patents and ethics: a Canadian solution

2000· other· en· W7073551630 on OpenAlexaboutno aff

Bibliographic record

VenueDigitalGeorgetown (Georgetown University Library) · 2000
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPatentabilityIncentiveIntellectual propertyPosition (finance)Process (computing)Subject (documents)Ethical issuesBioethicsPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

World Trade Organization member states are preparing for the upcoming renegotiation of the Agreement on Trade-Related Aspects of Intellectual Property Rights. One of the important elements of that renegotiation is the ethical considerations regarding the patenting of higher life forms and their component parts (e.g. DNA and cell- lines). The interface between the genetic revolution, patentability, and ethical considerations is the subject of this article. The author identifies, explores, and critiques four possible positions Canada may adopt in respect of patentability of biomedical material. First, Canada could do nothing. This approach would mean keeping biomedical materials outside the patent system and outside the stream of commerce. Canada would simply wait for an international consensus to develop before adopting a position of its own. Second, Canada could go it alone. It could implement a policy that balances the incentive effects of patents with the need to incorporate ethical and social values into the decision-making process regarding the use of biomedical materials. Third, Canada could follow the United States, Europe, and Japan by providing for almost unrestricted patenting of biomedical materials. This would be the most industry-friendly alternative. The fourth and final option is to use the medicare system to promote discussion of ethical considerations involved in the use of biomedical materials. The power of provincial health agencies may be used as a lever to ensure discussion of ethical considerations concerning the use of biomedical materials. The author concludes that the fourth and final option is the best alternative for Canada while waiting for an international consensus to emerge.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.194
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0230.034
Scholarly communication0.0240.014
Open science0.0030.011
Research integrity0.0250.021
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.012
GPT teacher head0.186
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueDigitalGeorgetown (Georgetown University Library)French-language works237,207