Recognizing Knowledge Mobilization: Lessons From a Canadian University Seeking to Redefine Research Impact Assessment
Bibliographic record
Abstract
Knowledge mobilization (KM) is an expected and recognized component of the research cycle. Canadian universities and other research institutions are increasingly establishing institutional supports for KM. However, few institutions have made progress on the assessment or recognition of KM activities in tenure and promotion processes. As such, despite the fact that researchers are encouraged and supported to engage in KM, such activities typically remain absent from their academic records and CVs. Uncertainty around the actual value of specific KM activities and a lack of templates or format standardization for recording and evaluating activities are two of the main reasons for this. A further complication is that most tenure and promotion committees (TPCs) are not well equipped to understand, interpret, and assess KM activities when they are reported. In this presentation, we discuss work initiated by Simon Fraser University to redefine research impact assessment in review, tenure, and promotion processes and develop supports and tools to address this gap. This includes: (1) a working group of grant facilitators, other research administrators, and librarians who are developing recommendations based on an exploration of past, current, and trending understandings and measures of research impact; (2) a tenure and promotion supports development team creating informational materials for faculty; and, (3) the KM unit conducting a scoping review of how KM activities are reported to inform tools and templates for faculty and TPCs. We will discuss the process and progress of this work, the implications of these strategies for research administration, and invite delegates to discuss their experience and interest in assessing research impact.
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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 | 0.174 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.050 | 0.022 |
| Scholarly communication | 0.033 | 0.013 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.008 | 0.013 |
| 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".