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Record W2021619142 · doi:10.2190/y8ur-ywvr-budp-cdr0

Intellectual Property Rights and the Canadian Pharmaceutical Marketplace: Where Do We Go from Here?

2005· article· en· W2021619142 on OpenAlexaffabout
Joel Lexchin

Bibliographic record

VenueInternational Journal of Health Services · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsIntellectual propertyOrder (exchange)Access to medicinesArgument (complex analysis)Pharmaceutical industryInvestment (military)International tradePatent ActLaw and economicsTRIPS architectureBusinessEconomicsPolitical scienceLawPatent lawMedicineFinancePharmacology

Abstract

fetched live from OpenAlex

Patent protection for prescription drugs has a long and contentious history in Canada. Bills C-22 and C-91, passed as part of Canada's commitment to various trade deals, first weakened and then abolished compulsory licensing. In order to decide on a future course of action that Canada should take on intellectual property rights (IPRs), it is useful to review downstream effects that resulted from C-22 and C-91. This article examines changes to employment, Canada's balance of trade in pharmaceuticals, investment in research and development, and drug expenditures. The author then reviews the arguments advanced by the pharmaceutical industry in favor of stronger protection for IPRs, the recent complaints made against Canada at the World Trade Organization regarding pharmaceutical IPRs, and the continuing argument about the "evergreening" of patents. Also discussed are the second-draft text agreement of the Free Trade Area of the Americas, which will, if implemented, have significant repercussions for pharmaceutical IPRs in Canada, and some ways in which patents distort the marketplace for drugs. The article concludes with some alternative recommendations on the future of IPRs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.833
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.316
Teacher spread0.278 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations24
Published2005
Admission routes2
Has abstractyes

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