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Record W1514933372 · doi:10.26443/mjm.v9i2.445

Scrutinized: The TRIPS Agreement and Public Health

2020· article· en· W1514933372 on OpenAlexaffvenue
Junaid Subhan

Bibliographic record

VenueMcGill Journal of Medicine · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntellectual propertyTRIPS architectureTRIPS AgreementLegislationAccess to medicinesLaw and economicsInternational tradeProduct (mathematics)MedicineBusinessLawPolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

The World Trade Organization's (WTO's) Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) of 1994 seeks to implement a uniform set of intellectual property protection across member nations to provide greater stability in international economic relations. Critics argue that the TRIPS agreement provides unnecessarily strong protection of intellectual property rights which serves to prevent the ill in developing nations from having access to affordable essential medications. The first recommendation that this paper makes is to provide two sets of intellectual property protection, one that applies to essential medications such as AIDS drugs and certain antibiotics and another that applies to drugs that treat non-life threatening conditions. The second recommendation builds upon the first recommendation: if two sets of intellectual property protection legislation are enacted, patents on essential medications should be restricted to patents on processes rather than the product itself. The third recommendation seeks to amend the language of the TRIPS agreement to make it obligatory for member nations to implement provisions on compulsory licensing within their domestic legislation.

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)0.022
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.066
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.024
Scholarly communication0.0200.014
Open science0.0040.009
Research integrity0.0500.026
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 head0.338
GPT teacher head0.275
Teacher spread0.063 · 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 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

Citations17
Published2020
Admission routes2
Has abstractyes

Explore more

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