Engaging knowledge users in Canadian knowledge mobilisation research: a scoping review of research in education
Bibliographic record
Abstract
Background: This study examines the engagement of knowledge users in knowledge mobilisation (KMb) research on Canadian K-12 teaching and education policy. Research on and around KMb has grown in the decade since this field was first assessed comprehensively. Thus, it is timely to re-evaluate if current knowledge producer-user relationships in KMb research feature the mediating variables or recursive elements promulgated as best practices in KMb research. Methods: A scoping review was conducted to identify the profile of knowledge users, map the engagement of knowledge users, and account for any changes to their roles in the research process since 2008. Twenty-eight relevant studies were identified. Contextual data and frequency of engagement with knowledge users were collected and analysed. Findings: Findings indicate that a diverse group of knowledge users are engaged in KMb research and draws on knowledge from various disciplines. A majority of the studies reported that knowledge users were engaged in at least two stages of their research process, with them most frequently engaged during the search and data collection phase of the research process. Discussion and conclusion: There has been an encouraging effort in building iterative producer-user connections with knowledge users being engaged, often repeatedly, across different phases of the research process. This indicates an increasingly collaborative model of soliciting user insights on the development and diffusion of research evidence. The review sets the foundation for potential future research on producer-user engagement and provides insights applicable beyond the Canadian K-12 education system.
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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 | 0.094 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.038 | 0.057 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".