How a Networked Approach to Building Capacity in Knowledge Mobilization Supports Research Impact
Bibliographic record
Abstract
Research impact is emerging as a common feature in national research systems. Knowledge mobilization (KMb) includes efforts undertaken to aid and accelerate research impact pathways by directing focus to processes that support impact. To date, researchers and universities have struggled to increase their capacity in KMb. This study explores the perceptions held by 16 leaders of Research Impact Canada, representing 14 networked universities, about the usefulness and use of networked learning to build institutional capacity in KMb. The analysis of data, which was collected using a mixed-methods survey design, highlights two overarching themes: 1) the contextual variability in how institutions engage in KMb work, and how practice-based subgroups can support the diverse KMb needs of different institutions; and 2) how capacity is developed through networked learning is distributed among individuals and groups within institutions, and how networked institutions need to be self-referential to the ways knowledge about KMb is sourced, validated, shared, interpreted, and employed.
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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 | 0.005 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".