Managing Knowledge and Identity across the Boundary of Academic and Commercial Science
Notice bibliographique
Résumé
In the last few decades, institutions of higher learning are being transformed from ivory towers to become engines of regional and national economic development and ‘knowledge businesses’ increasingly focused on producing commercial products for private industry. The role of academics is rapidly shifting as many in the professoriate are becoming ‘captured’ by an ethos of commercialization as they rush to bring the product of their research to the marketplace. Critics of the entrepreneurial paradigm see academics as promoters as well as victims of commercialisation who internalize the pursuit of profit and value of money under the academic capitalist knowledge regime. While some academic researchers have enthusiastically embraced the transformation in the relationship between science and business, and between the academy and industry, many remain firmly committed to academic science, disinterested in pursuing commercial opportunities. Yet, others choose a middle ground and straddle the academic and commercial boundary. The purpose of this paper is to illustrate the role of identity to influence how academic scientists manage the boundary between the world of academic science and commercial science. Drawing from a large sample of Canadian university academic researchers in the applied sciences (n=379), four distinct categories of academic scientists are identified: Type I: Traditional academics who view the realm of academic science and commercial science as distinct and choose to position themselves strictly as academic scientists; Type II: Pragmatic academic hybrids who view academic and commercial science as distinct but decide to strategically pursue industrial links to acquire resources that support their research; Type III: Collaborative academic hybrids who believe in the paramountcy of academic and industry collaborations for the advancement of science; and Type IV: Academic entrepreneurs who abide in the fundamental importance of academic-industry links for application and for commercial exploitation. Results suggest that our researcher categories are further differentiated with respect to the strength of their collaborations with industry, their program of research, the extent of their industry experience, the degree of financial support they receive from industry, the size of their research laboratory, and by their scientific publications and the number of patents and licenses they hold from their research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».