Knowledge Mobilization and Research Impact in Canadian Universities: A Developmental Evaluation of Network Learning
Bibliographic record
Abstract
In Canada and abroad, governments and research funders are increasing expectations that researchers engage in knowledge mobilization (KMb) efforts to advance the impacts of research. However, despite this growing pressure, researchers and universities are struggling to build their KMb capacity. This dissertation aims to contribute to the research and practice of KMb by investigating the experiences and perspectives university-embedded professional staff who operationalize KMb as well as their professional networks that build KMb capacity at scale. Developmental evaluation (DE) provides the overarching design of this study, and the participants were members of Research Impact Canada (RIC). A multi-manuscript format is used to organize this dissertation reflecting one study with three phases. The first paper corresponds to Phase I of the DE and presents survey findings from 16 RIC leaders, representing 14 networked universities, about the usefulness and use of network learning to build institutional KMb capacity. The second paper corresponds to Phase II of the DE and presents the findings from semi-structured interviews with 20 key informants from 17 RIC member institutions regarding network learning and how it could be structured to improve the disconnect between KMb theory and practice. The third paper combines the findings from Phases I and II of the DE and merges key insights with practitioner narratives from four experienced RIC members, focusing on how the lessons learned from RIC provide insight into how universities can foster active engagement in KMb. The final paper presents an overview of quantitative measurement tools for the impacts of co-production, which developed in response to an identified problem of practice for KMb professionals. Collectively, the findings from these papers elucidate the potential of network learning to build institutional KMb capacity, while acknowledging that such efforts require attention to (a) the diversity of individual and institutional practices; (b) tensions that can simultaneously spell network fragmentation or a generative learning environment; and (c) enduring questions for the field of KMb; and (d) how a necessary complement to KMb efforts will require engaging more openly and critically with psychometric and pragmatic considerations when designing, implementing, and reporting on research impact measurement tools.
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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 | 0.083 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".