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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".