Embaucher et former le personnel au sein de grappes ou de pôles d'entreprises
Bibliographic record
Abstract
Résumé Les grappes industrielles ou les pôles de compétitivité, qui sont des regroupements régionaux d’entreprises et/ou de centres de recherche sur une thématique industrielle donnée, sont considérés comme un fleuron de l’activité économique moderne. Toutefois, si leurs incidences positives sont maintenant reconnues, on ne sait pas encore très bien comment il faut gérer ces regroupements, et plus particulièrement en matière de gestion des ressources humaines (GRH), afin d’en optimiser les retombées. Cet article vise à analyser les particularités des pratiques d’embauche et de formation des ressources humaines dans les grappes industrielles et les pôles de compétitivité en France. Par la suite, nous donnons aux dirigeants, aux cadres et aux professionnels des conseils pour faire de la GRH un véritable levier d’amélioration de la performance et de la productivité des grappes industrielles et des pôles de compétitivité en France comme partout ailleurs. Fonctions : GRH, management, GOP, économie.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".