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Record W3201657439 · doi:10.3390/agronomy11091893

Crop Diversity Management System Commons: Revisiting the Role of Genebanks in the Network of Crop Diversity Actors

2021· article· en· W3201657439 on OpenAlexaff
Sélim Louafi, Mathieu Thomas, Elsa Berthet, Flora Pélissier, Killian Vaing, Frédérique Jankowski, Didier Bazile, Jean‐Louis Pham, Morgane Leclercq

Bibliographic record

VenueAgronomy · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsCommonsCorporate governanceContext (archaeology)StakeholderDiversity (politics)Conceptual frameworkKnowledge managementTragedy of the commonsEnvironmental resource managementSociologyPolitical sciencePublic relationsSocial scienceEcologyGeographyEconomicsComputer scienceBiologyManagement

Abstract

fetched live from OpenAlex

This paper rethinks the governance of genebanks in a social and political context that has significantly evolved since their establishment. The theoretical basis for the paper is the commons conceptual framework in relation to both seed and plant genetic resources. This framework is applied to question the current policy ecosystem of genetic research and breeding and explore different collective governance models. The concept of crop diversity management system (CDMS) commons is proposed as the new foundation for a more holistic and inclusive framework for crop diversity management, that covers a broad range of concerns and requires different actors. The paper presents a multi-stakeholder process established within the context of the two recent projects CoEx and Dynaversity, imagining possible collective arrangements to overcome existing deadlocks, foster collective learning, and design collaborative relationships among genebanks, researchers, and farmers’ civil society organizations involved in crop diversity management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.387

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 science0.0010.002
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 head0.026
GPT teacher head0.210
Teacher spread0.184 · 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 designObservational
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

Citations16
Published2021
Admission routes1
Has abstractyes

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