Innovation ouverte et écosystème d’innovation : Implications pour le secteur public
Bibliographic record
Abstract
Le déploiement d’initiatives d’innovation ouverte représente, pour tous types d’organisations et plus particulièrement pour les organisations du secteur public (OSP), une aventure risquée et exigeante. La présente étude a identifié 17 défis, regroupés dans quatre catégories, liés au déploiement d’IO : 1) Collaboration inter-organisationnelle, 2) Processus d’innovation, 3) Implication des citoyens et 4) Données et technologies, ainsi que 18 bonnes pratiques/actions organisationnelles permettant d’augmenter les probabilités de succès. Finalement, deux études de cas, PULSAR et Cité de l’innovation et des savoirs Aix-Marseille (CISAM) , ont été réalisées, afin de mieux comprendre comment ces pratiques peuvent répondre aux défis, tout en en favorisant la création de valeur lors du cycle d'innovation.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".