A Knowledge Mobilization Initiative Pilot in the Library
Bibliographic record
Abstract
Over the last twenty years knowledge mobilization (KM) is increasingly a priority for researchers, funders, and universities. As KM emphasizes non-traditional forms of mobilization and encourages approaching research differently (e.g. co-production) there is a natural fit with advancements in academic librarianship such as digital scholarship. The goal of KM is to increase the use and positive impact of research beyond academia. Many researchers, required to plan and do KM as part of the funding requirements, need additional supports to learn about and mobilize their research beyond traditional approaches; academic institutions are responding with developing support services or roles in the institution. Approaches to these services are diverse, some centralized, some faculty or department specific, some dedicated roles, others added on to existing roles. In this paper we describe a pilot project to support KM at one Canadian university. Specifically, we share the development, initiation, and program model of a KM support unit within an academic library. We make the case for the importance of physical location of this type of service, the value the library adds to this service, and other lessons learned through this pilot project.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".