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Record W2253036634

Open Source Approaches in Biotechnology: Utopia Revisited

2006· article· en· W2253036634 on OpenAlexaff
Yann Joly

Bibliographic record

VenueMaine law review · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsInefficiencyArgument (complex analysis)Field (mathematics)Open sourceCriticismOpen innovationManagement scienceBusinessBiotechnologyEconomicsComputer sciencePolitical scienceMarketingLawBiology
DOInot available

Abstract

fetched live from OpenAlex

Tracing its origin to Greek antiquity, intellectual property has become an institution in modern legal systems worldwide. This growing importance of intellectual property was confirmed with the 1994 adoption of the Trade-Related Aspects of Intellectual Property Rights Agreement by the World Trade Organization (WTO), which harmonized the rules of intellectual property amongst the various members of the international community on the model of developed countries. However enshrined in the legal tradition, intellectual property law has also had its share of detractors and has recently come under severe criticism. The exercise of intellectual property rights in such diverse fields of creation as music, information technology, and biotechnology has met with intense opposition from a growing number of detractors. In the field of biotechnology, the critique has become important enough to arouse the attention of a number of legislative bodies and propel the creation of an important corpus of normative documents (recommendations, position statements, declarations, etc.). Surprisingly, this legislative outburst, aimed at correcting certain deficiencies of the patent system, was driven by a number of theoretical hypotheses that were unconfirmed by the available evidence. Various solutions have been proposed in these normative documents to palliate certain presumed failings of the patent system: compulsory licenses, adoption of moratoria on gene patents, parallel imports, and more restrictive evaluation of patent applications. Alongside these policy solutions, the use of cooperative strategies to facilitate the use of patented inventions has become a particularly popular alternative in academia. It has been suggested that cooperative strategies-such as open source, patent pools, and defensive publication-could correct the inadequacies generated by the application of the patent system to biotechnological inventions without requiring a major change in current intellectual property laws. Thus, the main justification invoked in favor of the introduction of open source approaches in biotechnology is that it would remedy the various failings of the patent system. The numerous articles discussing these approaches all follow a similar structure. The author usually begins by discussing the idyllic culture of open science that is said to have prevailed in the pre-1980 academic biomedical research field, and expresses his or her regret at the recent commercialization of academia and its adverse effect on fundamental research. He or she then advances his or her central argument in favor of open source as a solution to the possible existence of an “anticommons effect” in biomedical research that could slow down or possibly immobilize the progress of science. After reassuring readers that the introduction of open source approaches would likely prevent such a catastrophic scenario, the article ends on a positive note by evasively mentioning some of the more intrinsic benefits of these approaches. It is not necessarily prudent for proponents of cooperative strategies to use, as a central part of their argumentation, a negative discourse that focuses largely on hypothetical risks unsubstantiated by the available empirical evidence. A better strategy would be to identify and promote the wealth of intrinsic benefits associated with these strategies in order to keep them attractive, independent from any evaluation of the patent system.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.915
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.295
Teacher spread0.276 · 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.

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

Citations6
Published2006
Admission routes1
Has abstractyes

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