Critical Knowledge Mobilization: Directions for Social Gerontology
Bibliographic record
Abstract
The concept of knowledge mobilization (KMb) is prominent in governance frameworks of tri-council funding in Canada. Yet there are a number of conceptual and practical challenges when such ideas are proposed for adoption across large multidisciplinary contexts. This research note introduces the concept of critical knowledge mobilization as a way to understand KMb in large multidisciplinary teams and social gerontology. It begins with a high-level sketch of the historic changes in knowledge production and knowledge sharing, followed by a definition of critical knowledge mobilization and examples of historical ideas and everyday tensions in practice. Building on these, we propose the need to advance and shift the culture of KMb, and to embark on engaged research as a means of innovation. We suggest that a reflexive process of critical KMb can facilitate innovation and promote a culture of knowledge mobilization in Canadian social gerontology.
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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 | 0.104 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.033 | 0.164 |
| Scholarly communication | 0.049 | 0.053 |
| Open science | 0.009 | 0.041 |
| Research integrity | 0.025 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 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".