Open innovation in the life science industry: drivers, barriers and importance to be part of a cluster.
Bibliographic record
Abstract
Sommario Questo lavoro di tesi è stato realizzato grazie ad un internship di sei mesi presso l’École Polytechnique de Montréal. Obiettivo del progetto è indagare in che modo le aziende, che appartengono al settore del Life Science, attuino pratiche di open innovation: quali sono i principali driver; quali le barriere e quale il vantaggio per le imprese appartenenti a un cluster. Per investigare quanto sopra è stata realizzata una survey di diciassette domande a risposta chiusa che è stata poi inviata alle aziende che appartengono ai cluster del Québec e della Toscana. È stata fatta, quindi, una comparazione tra i risultati ottenuti attraverso questo progetto e quelli ottenuti da una precedente ricerca effettuata in Danimarca, all’interno della Medicon Valley. Abstract This thesis work has been made possible thanks to a six-month internship at the École Polytechnique de Montréal. The project aims to explore how companies, which belong to the field of Life Science, implement practices of open innovation: what are the main driver; what barriers and what the advantage for companies belonging to a cluster. To investigate the above it was carried out a survey of seventeen closed questions that was then sent to companies that belong to the Quebec and Tuscany cluster. It was made, therefore, a comparison between the results obtained through this project and those obtained from previous research carried out in Denmark, in the Medicon Valley.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".