Modéliser une grappe industrielle de compétences : le cas des entreprises de biotechnologie de la région de Montréal
Bibliographic record
Abstract
Résumé Déjà reconnu comme un centre de calibre international dans les sciences de la vie et de la santé, Montréal s’affirme aujourd’hui comme un pôle d’activité mondial en biotechnologie. Pour décrire et expliquer ce phénomène, nous avons analysé les quatre dynamiques porteuses de valeur dans une grappe industrielle : la capitalisation du savoir, l’optimisation des coûts, la double logique de la concurrence et de la coopération ainsi que la dynamique de gouvernance des firmes. Dans la grappe industrielle de firmes de biotechnologie de Montréal, ces dynamiques se fondent sur sept moteurs stratégiques essentiels : capitalisation du savoir cristallisée par des centres universitaires (alliances) et autour d’eux (essaimage), optimisation des coûts ancrée dans des avantages fiscaux pour la R&D, concurrence pour l’accès aux ressources humaines qualifiées et coopération pour la construction d’actifs complémentaires (de R&D ou de commercialisation), gouvernance stratégique locale et gouvernance scientifique internationale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".