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Record W4403240479 · doi:10.3917/ror.193.0063

Construire un commun d’énergie renouvelable. Analyse de trois projets en codéveloppement

2024· article· fr· W4403240479 on OpenAlexaff
Amélie Artis, Justine Ballon

Bibliographic record

VenueRevue de l’organisation responsable · 2024
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsRenewable energyDevelopment (topology)Environmental economicsEconomicsEngineeringMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Parmi les projets d’énergies renouvelables (ENR) participatifs français, émergent, depuis le milieu des années 2010, des projets dits en codéveloppement, associant un développeur privé, des citoyens en association et une collectivité territoriale. Encore peu étudiés, cet article en propose une analyse autour de la question suivante : en quoi le processus de coopération soutenant les projets d’ENR en codéveloppement constitue une forme de commoning de la production d’énergie participant à la « transition énergétique citoyenne » ? À partir d’une étude de trois cas, et par le prisme du commoning, nous analysons la construction sociale d’une coopération entre des acteurs hétérogènes visant la gestion collective de la production d’ENR. Nous montrons que, dans ces projets, la fabrique de communs énergétiques repose sur cinq variables : une communauté hétérogène coopérante, l’articulation de logiques plurielles (marchandes, publiques et réciprocitaires), une territorialisation des ressources, une gouvernance démocratique et la conflictualité, à condition d’être le vecteur de compromis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.008
Scholarly communication0.0080.005
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.254
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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