Knowledge Mobilization in Ontario: A Multi-case Study of Education Organizations
Bibliographic record
Abstract
In recent decades, there has been growing interest among governments and research funders to mobilize knowledge and strengthen evidence-informed decision-making. Knowledge mobilization (KMb), the process of connecting research to policy and practice, is about individual and organization-level efforts to increase the use of research findings by education stakeholders such as policymakers, practitioners and the public. Using a multi-case design (Stake, 2006; Yin, 2014;), this study draws from the KMb literature, examines the contextual factors affecting organizational KMb (social and political context, mission, culture, and capacity) and analyzes the KMb approaches and activities in organizations (purpose, evidence production, target audience, strategies, mediation, impact, and challenges). The sample consists of four different education organizations within the province of Ontario, Canada: a university (York University), an urban school board (Toronto District School Board), a professional teacher organization (Ontario College of Teachers), and a non-profit (People for Education). Data sources include publicly available documents on organizational websites (e.g., products, events, networks, and capacity-building). Key informant interviews (N =18) were conducted with senior leadership and researchers in order to gain insight into the KMb approaches and activities. Overall, the organizations differed greatly not only in their mission, culture and capacity for KMb, but, also, in their understanding of KMb. This study identified ten common challenges to KMb, which included limitations to the organizational culture and capacity for KMb, a misalignment between the strategic direction and organizational mandate, and a limited understanding of dissemination mechanisms. Altogether, measures of impact were found to be weak across the cases. The study makes recommendations for strengthening KMb efforts in organizations and across the education sector. The results may help educators, researchers and policymakers understand how to develop and enhance efforts to mobilize research knowledge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".