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Record W2785564061 · doi:10.7202/1052690ar

Spécificités des sources de connaissances pour l’innovation environnementale des PME

2018· article· fr· W2785564061 on OpenAlexvenueno aff
Amandine Pinget, Rachel Bocquet

Bibliographic record

VenueManagement international · 2018
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesBusinessArt

Abstract

fetched live from OpenAlex

Ancrée dans la perspectiveknowledge-based, cette recherche vise à mettre en évidence les spécificités des sources de connaissances des PME pour les innovations environnementales qui restent peu étudiées comparativement aux innovations technologiques. Les résultats montrent que les sources de connaissances, essentielles pour les innovations environnementales des PME, diffèrent de celles mobilisées pour les innovations technologiques. Les PME qui introduisent des innovations environnementales font davantage appel aux sources externes qu’internes de connaissances. Cette recherche conduit à formuler des recommandations utiles aux PME et aux acteurs en charge de leur développement.

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)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.007
Science and technology studies0.0020.005
Scholarly communication0.0160.010
Open science0.0010.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.029
GPT teacher head0.261
Teacher spread0.232 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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