Institutional Systems & Structures for Knowledge Mobilization: Bringing Worlds Together to Support Knowledge Mobilization
Bibliographic record
Abstract
In this roundtable, we explore developing structures for knowledge mobilization in two different institutional systems, a university and in health and social care. First, we will explore developing a knowledge mobilization hub at Simon Fraser University, exploring consideration for spaces, sources, and supports. We will discuss questions space in the context of remote working, of the future roles of institutional libraries, and how to assess the value and impact of knowledge mobilization supports. Next, will consider the delivery of safe, effective, person centred care and how a systems approach must be developed to maximise knowledge use. The structures, processes and culture change that are required to mobilize knowledge from research, experience and practice to develop into ‘learning systems’ will be explored.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.012 |
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".