A report detailing the development of a university-based knowledge mobilization unit that enhances research outreach and engagement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it