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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0140.006

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; both teacher heads agree on what is shown here.

Study designNot applicable
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

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