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Record W2896690052

Penser et organiser l'accompagnement de l'innovation collective dans l'agriculture

2018· preprint· fr· W2896690052 on OpenAlexfundno aff
Aurélie Toillier, Guy Faure, Eduardo Chia

Bibliographic record

VenueAgritrop (Cirad) · 2018
Typepreprint
Languagefr
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
FundersMinistry of Agriculture - Saskatchewan
KeywordsPolitical scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

Ce chapitre rend compte des différentes fonctions des dispositifs d’appui à l’innovation existants aujourd’hui dans les secteurs de l’agriculture et de l’agroalimentaire au Sud, afin d’identifier les contributions possibles de la recherche à leur renforcement. Les auteurs montrent qu’une diversité de dispositifs est nécessaire pour créer des conditions favorables à l’innovation et pour accompagner pas à pas des communautés d’innovation, en fonction de leurs capacités et de leurs besoins d’apprentissage. Les chercheurs sont incités à sortir de leur rôle classique de producteurs de connaissances ou de formateurs, pour s’impliquer davantage aux côtés des acteurs de l’accompagnement. Ils peuvent alors produire des connaissances sur les mécanismes d’innovation et sur les dispositifs d’accompagnement eux-mêmes, permettant d’aider à penser et organiser l’accompagnement de l’innovation dans une diversité de situations de gestion.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.003

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 head0.025
GPT teacher head0.261
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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