Piloter les projets d'innovation au sein des pôles de compétitivité: Des leviers managériaux à mobiliser
Bibliographic record
Abstract
Au sein des poles de competitivite francais, des projets collaboratifs font travailler ensemble, sur une meme zone geographique, des salaries de PME, de grands groupes et de laboratoires publics, autant d’acteurs relevant d’objectifs, de cultures professionnelles et de systemes de management tres differents. Cet article propose un modele d’analyse de la collaboration dans ce contexte specifique. L’etude comparative de deux projets en cours dans un meme pole de competitivite, met en lumiere la variete possible des situations et des pratiques de pilotage. Elle met egalement en evidence les besoins encore non satisfaits en la matiere, ce qui nous amene a proposer des leviers manageriaux et humains pour ce type de projets et de contextes jusqu’ici peu envisages.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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.
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".