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Record W1495075743

Open Innovation in Plant Genetic Resources for Food and Agriculture

2013· article· en· W1495075743 on OpenAlexaff
Chidi Oguamanam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIntellectual propertyOpen innovationContext (archaeology)Promotion (chess)BusinessOrder (exchange)Public goodInterdependenceOpen educational resourcesLicenseAgricultureKnowledge managementPublic relationsPolitical scienceMarketingEconomicsSociologyComputer scienceGeographyLaw
DOInot available

Abstract

fetched live from OpenAlex

Contemporary global order for the promotion of innovation exaggerates the role of intellectual property (IP) as a closed proprietary model of knowledge production and protection. Partly as a boomerang effect of that order or partly as a coincidence of the phenomenal rise in the information and communication technologies or both, there has been increased gravitation toward open, collaborative, shared, communal and interdependent models of innovation. This trend is typified by the rise of open software movement and cognate endeavours. The article attempts to transpose the open innovation dynamic to the context of plant genetic resources for food and agriculture (PGFA); and draws attention to the customary seed sharing and exchange as the centre-piece of the inherent open nature of innovation in agriculture, especially in indigenous and local communities. Focusing on the emergent institutional and legal frameworks for the governance of PGRFA, the article finds that they reflect pragmatic attempts at melding both the IP-driven closed model and the accommodation of open or public goods approach toward the promotion of access and overall management of innovation in PGRFA. It concludes that IP is not necessarily antithetical to open innovation, but could be calibrated to advance it.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.417

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 communication0.0000.001
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 head0.019
GPT teacher head0.210
Teacher spread0.191 · 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 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
Published2013
Admission routes1
Has abstractyes

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