Bridging knowledge mobilization and inclusion by developing a community of practice DEI action plan
Bibliographic record
Abstract
Purpose This Viewpoint article presents the intersection of knowledge mobilization and diversity, equity and inclusion (DEI) from the perspective of knowledge mobilization practitioners. We represent the Knowledge Mobilization Unit at York University (Toronto, Canada) and Research Impact Canada, Canada’s knowledge mobilization network. We reflect on building and animating a Community of Practice (CoP) with the Future Skills Centre and outline the DEI Action Plan we developed for that knowledge mobilization mechanism. We provide recommendations for researchers and research organizations to strengthen the role of DEI in knowledge mobilization. Design/methodology/approach We provide critical inquiry into our knowledge mobilization practices through self-reflection, comparison to the literature, and testing against the lived and living experiences of knowledge mobilization and DEI practitioners. Findings We outline the steps taken to build the CoP and develop and implement the DEI Action Plan to support peer exchange and learning, collaboration, and capacity building. We also conclude that knowledge mobilization and DEI are mutually reinforcing. Both seek excellence in diverse forms. Both seek to maximize access to research programs, outputs and evidence. Both are common features in the Canadian research landscape. Originality/value The intersections of knowledge mobilization and DEI are only starting to be explored. As a viewpoint article, we have written from our perspective of knowledge mobilization practitioners who bring diverse personal and professional DEI perspectives to our work. This complements the literature review conducted by Cornelius-Hernandez and Clark (2024) with recommendations derived from our practice.
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 | 0.100 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".