Knowledge Mobilization, Collaboration, and Social Innovation: Leveraging Investments in Higher Education
Bibliographic record
Abstract
ABSTRACT This article is a qualitative literature synthesis in the areas of community-campus collaborations, knowledge mobilization and social innovation. The article aims to be useful to people who work in academic settings, community organizations, public institutions, and government. The authors utilized a purposive sampling methodology to explore the following questions: 1. How can university-based knowledge mobilization leverage investments in higher education research and development (R&D) through community-campus collaboration and social innovation? 2. What is the role of university-wide knowledge mobilization projects in supporting community-campus connections and ultimately social innovation strategies that contribute to the public good? Our review indicates considerable interplay between community-campus collaborations, knowledge mobilization and social innovation given that knowledge mobilization facilitates – and is facilitated by – collaboration. With sufficient knowledge mobilization, community-campus collaborations stimulate social innovation. The article concludes with recommendations based on our review of the literature. RÉSUMÉ Cet article se fonde sur une synthèse littéraire qualitative portant sur les collaborations communautaires/académiques, la mobilisation du savoir et l’innovation sociale. Il se veut utile pour toute personne travaillant dans un milieu académique, un organisme communautaire ou une institution publique. Les auteurs ont recours à une méthode d’échantillonnage raisonné pour répondre aux questions suivantes : 1. Comment la mobilisation du savoir universitaire – au moyen de la collaboration communautaire/académique et de l’innovation sociale – peut-elle faire augmenter les investissements en recherche et développement dans l’enseignement supérieur? 2. Comment les projets de mobilisation du savoir universitaire peuvent-ils resserrer les liens entre campus et communauté et, en fin de compte, appuyer des stratégies d’innovation sociale qui contribuent au bien commun? Notre évaluation indique qu’il y a beaucoup d’influences réciproques entre les collaborations communautaires/académiques, la mobilisation du savoir et l’innovation sociale, surtout que la mobilisation du savoir facilite la collaboration et vice versa. En effet, avec une mobilisation du savoir suffisante, les collaborations communautaires/académiques stimulent l’innovation sociale. Cet article se termine par des recommandations provenant de notre analyse documentaire.
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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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".