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Record W1995683175

Patents and Pharmaceutical R&D: Consolidating Private-Public Partnership Approach to Global Public Health Crises

2010· article· en· W1995683175 on OpenAlexaff
Chidi Oguamanam

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicPharmaceutical Economics and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual propertyIncentiveGeneral partnershipCredibilityBusinessLaw and economicsPublic economicsPublic relationsPublic administrationEconomicsPolitical scienceMarket economyLawFinance
DOInot available

Abstract

fetched live from OpenAlex

Intellectual property (IP) is a reward and incentive market-driven mechanism for fostering innovation and creativity. The underlying, but disputed, assumption to this logic is that without IP, the wheel of innovation and inventiveness may grind to a halt or spin at a lower and unhelpful pace. This conventional justification of IP enjoys, perhaps, greater empirical credibility with the patent regime than with other regimes. Despite the inconclusive role of patents as a stimulant for research and development (R&D), special exception is given to patent’s positive impact on innovation and inventiveness in the pharmaceutical sector. This article focuses on that sector and links the palpable disconnect between the current pharmaceutical R&D agenda and global public health crises, especially access to drugs for needy populations, to a flaw in the reward and incentive theory of the patent system. It proposes a creative access model to the benefits of pharmaceutical research by pointing in the direction of a global treaty to empower and institutionalize private-public partnerships in health care provisions. Such a regime would restore balance in the global IP system that presently undermines the public-regarding considerations in IP jurisprudence.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.157
GPT teacher head0.352
Teacher spread0.194 · 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 designTheoretical or conceptual
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

Citations9
Published2010
Admission routes1
Has abstractyes

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