Institutionally Embedded Professionals’ Perspectives on Knowledge Mobilization: Findings from a Developmental Evaluation
Bibliographic record
Abstract
Intentional efforts to advance the societal impacts of research are known as knowledge mobilization (KMb). Despite increas-ing pressure on researchers and higher education institutions (HEIs) to engage in KMb activities, capacity building in this area is constrained by a limited understanding of the role of HEIs from the perspective of embedded KMb professionals. This study presents findings from a developmental evaluation of Research Impact Canada’s efforts to build institutional capacity for KMb. Through semi-structured interviews (n = 20) with KMb professionals from 15 Canadian HEIs, we share (a) approaches for how KMb professionals can thrive in institutional environments, and (b) essential questions about KMb for the higher edu-cation sector. From that basis, we discuss how there is a need for skilled KMb professionals within HEIs and a need for (inter)national research and practice collaborations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.099 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| 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".