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 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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".