Collaboration in Academia: Motives, Forms and Impacts on scientific Productivity
Notice bibliographique
Résumé
With the Internet becoming readily available and cheaper, even in the most remote corners of the world, opportunities for exchanges of all kinds are more and more wide spread. This is true for the exchange of ideas, results and research methods. Many scientists are taking advantage of these new opportunities by working collaboratively with peers, industry and governments both locally or in further places. The review of the literature on such activities clearly shows that industry-university collaboration in applied engineering receives the bulk of the attention. As well, these previous studies mainly focus on mechanisms and the financial costs and benefits of collaborative research. They thus leave out the impact of collaboration on academic productivity. Some of these studies also claim that collaborative research is just a pretext for generating additional research funds with minimum coordination. Others further contend that increased collaboration by scientists and educators will ultimately bastardize the traditional mission of universities, i.e. producing graduates and generating new knowledge and ideas. While these previous studies shed useful lights on scientists' collaborative activities, by mainly focusing on applied engineering, they are limited in scope. They also overlook many of the motives behind collaborative activities in academia. These omissions are in addition to the limited attention they pay to the impacts of collaboration on scientists' productivity. Using information from a survey of 1,566 scientists from all scientific disciplines in Québec, Canada, our study tries to overcome some of these limitations by i) looking at collaboration by scientists from all scientific disciplines; ii) by accounting and comparing collaborative activities among 1) researchers; 2) with industry; and 3) with other institutions, namely, governments and organized interest groups and finally iii) by assessing the impacts of these collaborative activities on scientific production. The results show that collaboration is prevalent in all scientific disciplines even though scientists in humanities have fewer collaborative output than others. As well, motives for collaboration are of three types: strategic, organisational and operational. Furthermore, even though academic collaboration intended to produce patented or unpatented products, scientific instruments, softwares and artistic products has few output, collaboration ultimately increases productivity, regardless of the discipline or the partners. Thus, university administrators aided by NRENs, government decision-makers and other donors should work at creating and enabling a collaborative friendly environment for academic researchers.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».