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Record W4401345632 · doi:10.1111/jwip.12321

TRIPS and the right to human health: A case study on Brazil's health policies and its implications

2024· article· en· W4401345632 on OpenAlexaff
Marcella Rocha dos Reis

Bibliographic record

VenueThe Journal of World Intellectual Property · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIntellectual propertyRight to healthTRIPS architectureTRIPS AgreementGovernment (linguistics)Human rightsDecreeEconomic growthPrerogativePublic healthHealth carePolitical scienceBusinessPoliticsLawEconomicsMedicine

Abstract

fetched live from OpenAlex

Abstract The incorporation of intellectual property laws in Brazil, by drafting or amending laws, as a result of the Trade‐Related Aspects of Intellectual Property Rights (TRIPS) Agreement, has been a subject of political and social debate. The ambiguity of the Agreement's effects explains the Brazilian government's reluctance to address the issue, particularly concerning public health policies, as it presents a conflict between healthcare rights and intellectual property protection. The country had utilized compulsory licensing of an antiretroviral drug for AIDS treatment under Decree No. 6,108/2007. This precedent has sparked discussions about future cases of selective incorporation of compulsory licensing as a national prerogative, aiming to ensure that the patent system upholds the country's right to protect public health and promote access to medicines, in accordance with The Doha Declaration on the TRIPS Agreement. Nevertheless, such a pattern could undermine innovation and discourage private‐sector investment and research, which is predominantly conducted by developed countries.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.007
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.166
GPT teacher head0.333
Teacher spread0.167 · 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 designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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