Imagining the Future of Knowledge Mobilization : Perspectives from UNESCO Chairs
Bibliographic record
Abstract
These themes weave through a new portfolio of thought leadership papers reflecting on the subject of Knowledge Mobilization (KMb): the process described by the Social Sciences and Humanities Research Council of Canada (SSHRC) as “encompassing a wide range of activities relating to the production and use of research results, including knowledge synthesis, dissemination, transfer, exchange, and co-creation or co-production by researchers and knowledge users.”1 Such activities, and others referenced in the papers written by seven members (six Canadian and one German) of the UNESCO Chairs network, aim to bridge the sometimes-deep divide between the creation of new knowledge and its application for social benefit. As several of these papers note, the KMb enterprise has assumed heightened importance in the face of the COVID-19 pandemic and other grand challenges confronting humanity. But interest in KMb is not new, and a body of experience lies ready to inform efforts to learn and improve.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.039 | 0.050 |
| Scholarly communication | 0.033 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".