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Record W7057365142

International Rights Affecting the COVID–19 Vaccine Race

2022· article· en· W7057365142 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyTRIPS architectureHuman rightsOrder (exchange)PandemicMonopolizationInventionProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

The impact of the COVID–19 pandemic has been felt world-wide, and despite having several vaccines in the market at this point, there are still issues of accessibility for certain countries. International intellectual property law has been a breeding ground for the exploration of intellectual curiosity and creation as it provides strong protections to creators. These strong protections have allowed for the monopolization of certain goods, such as vaccines, under the concept of patents. While patents are important to incentivize pharmaceutical companies to create life–saving medicines, these protections have also become a barrier for access to medicines, especially in less–developed countries. This Note seeks to address the interplay between international intellectual property rights and the right to health under the inter-national human rights framework. Specifically, it will dis-cuss the two differing rights through the United States and Canada’s efforts to promote creation of COVID–19 vaccine candidates. In order to highlight the financial driver behind patent protections, this note will compare the production and patenting process of the COVID–19 vaccines, a virus that also heavily impacted developed countries, versus the under–funded Ebola virus, which predominantly effected less–developed countries. Finally, this Note will offer recommendations on how countries, and pharmaceutical companies, can take a human rights approach by utilizing patent protection exceptions in order to make COVID–19 vaccines accessible to all 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 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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.704
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.6070.000

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 head0.013
GPT teacher head0.277
Teacher spread0.264 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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