Pratiques innovatrices des PME rhône-alpines : une approche par la diversité des sources de l'innovation technologique
Bibliographic record
Abstract
Cet article vise à décrire le comportement des petites et moyennes entre- prises (PME de 20 à 499 salariés) rhône-alpines en matière de création et d’acquisition des connaissances technoscientifiques nécessaires à la réalisation des innovations technologiques. Pour mener à bien cette analyse, nous avons utilisé les résultats d’une régionalisation de l’enquête innovation du SESSI (1990). On montre dans un premier temps que le critère de la taille des entreprises innovantes permet d’identifier des comportements types qui opposent les PME aux grandes entreprises. Mais cette homogénéité cache en réalité une diversité comportementale des PME innovatrices mise en évidence lors de l'analyse sous-sectorielle. On identifiera notamment trois grands types de sous-secteurs se caractérisant par le développement de pratiques d’acquisition de connaissances technologiques externes distinctes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".