A report detailing the development of a university-based knowledge mobilization unit that enhances research outreach and engagement
Bibliographic record
Abstract
This field note presents reflections from the perspective of a knowledge mobilization (KMb) practitioner after 5 years of developing and delivering KMb services in a university-based environment. This field note is a “how to” based on experience from the field of KMb practice and places that experience in the context of academic literature. The paper concludes that KMb is not a single event or process but a system, a suite of services that work together to support the multi-directional connection of researchers with decision makers. The six KMb services that comprise the KMb system are informed by four broad KMb methods: producer push, user pull, knowledge exchange and co-production. Examples of each KMb service are provided along with key observations that allow others interested in developing institutional KMb support services to implement these services in their own context. The field note concludes with clear recommendations for individuals and organizations interested in developing their own system of KMb services.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".