Research Impact on the Move: A Study of Capacity Development for Reciprocal Knowledge Mobilization in Canada
Notice bibliographique
Résumé
This doctoral thesis provides a systematic investigation into the capacity development for knowledge mobilization (KMb) within the field of education. Despite the substantial body of research evidence that could significantly enhance educational outcomes, there remains a considerable disconnection between these findings and their practical application. This doctoral study investigates the challenges and mechanisms of KMb, focusing on capacity development and reciprocity as pivotal elements in bridging this gap.The manuscript-based study adopts a multi-level approach to identify and address the gaps in the literature on capacity development for KMb and reciprocity of KMb relationships between academics and community-based organizations (CBOs). This approach involves a systematic scoping review of the related literature, followed by qualitative interviews with end beneficiaries such as graduate students and CBOs. The scoping review methodology (Chapter 3) delves into the nuances of capacity development for KMb processes and emphasizes the need for tailored initiatives that align with specific contextual needs and challenges. The chapter also highlights a pervasive inconsistency between the articulated goals of research organizations and their actual KMb capacity development practices, potentially undermining the efficacy of KMb efforts. It calls for a more integrated and systematic approach to enhance the accessibility and availability of capacity support. Informed by the gaps in the literature and the inconsistency between research evidence and current practices of capacity development for KMb, the doctoral research explores the challenges faced by graduate students in the Faculty of Education at McGill University as they engage in KMb activities (Chapter 4). It particularly focuses on capacity development needs within the context of Canadian higher education. The study uses a qualitative case study approach to gain an in-depth understanding and capture the nuances of experiences. It identifies substantial barriers to effective engagement in KMb activities due to inadequate organizational support and misaligned incentive structures. Furthermore, the research underscores the importance of developing KMb capacities tailored to graduate students' specific needs, including connecting and engaging with non-university partners and the practical application of research findings.As Chapters 3 and 4 emphasize a relational approach to building capacities for KMb and creating supportive infrastructures for active engagement with non-university partners, the study in Chapter 5 progresses to incorporate a more critical approach to KMb by drawing on the concept of reciprocity. It investigates the viewpoints and perspectives of CBOs in Montreal about their challenges in participating in KMb and receiving benefits for their contributions to the KMb process. This approach acknowledges the unique and strategic position of community organizations in amplifying the reach of research evidence while emphasizing the need for more beneficial and equitable arrangements for KMb structures. The study's findings highlight several barriers to effective KMb engagement for community organizations, including limited access to resources, insufficient training in research, and a lack of recognition of the value of community knowledge.In conclusion, this doctoral research provides a comprehensive understanding of the systemic challenges and opportunities in KMb across academic and non-academic domains, advocating for a more inclusive and effective KMb ecosystem. It offers practical strategies to enhance KMb practices through capacity development and reciprocal KMb relationships. It calls for an integrated approach to capacity development that views KMb not only through a technical academic lens but also as a social process that recognizes the unique needs and benefits of non-university partners
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,049 | 0,013 |
| Communication savante | 0,013 | 0,004 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».