Spécificités des coopérations en R&D subventionnées et non subventionnées dans la stratégie partenariale d’EDF R&D
Bibliographic record
Abstract
Ce travail analyse la place respective des coopérations en R&D subventionnées versus non subventionnées dans les stratégies d’accords technologiques des entreprises. Nous avançons que les ressources additionnelles apportées par les subventions gouvernementales favorisent la création d’un portefeuille de coopération ambidextre, les accords subventionnés étant plus exploratoires que les seconds, lesquels sont plutôt à visée d’exploitation. Puis nous élaborons une série de propositions théoriques permettant de différencier les deux types d’accords suivant deux logiques organisationnelles distinctes, en termes d’incitation, de coordination et d’apprentissage. L’ensemble de nos propositions est confronté de manière probante au cas de la stratégie partenariale d’EDF R&D.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".