Knowledge mobilization with and for equity-deserving communities invested in research: A scoping review protocol
Bibliographic record
Abstract
The practice of putting research into action is known by various names, depending on disciplinary norms. Knowledge mobilization, translation, and transfer (collectively referred to as K*) are three common terminologies used in research literature. Knowledge-to-action opportunities and gaps in academic research often remain obscure to non-academic community partners and researchers in communities, policy and decision makers, and practitioners who could benefit from up-to-date information on health and wellbeing. Academic research training, funding, and performance metrics rarely prioritize or address non-academic community needs from research. We propose to conduct a scoping review on reported K* in community-driven research contexts, examining the governance, processes, methods, and benefits of K*, and mapping who, what, where, and when K* terminology is used. This protocol paper outlines our approach to gathering, screening, analyzing, and reporting on available published literature from four databases.
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.
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 | 0.279 | 0.233 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.068 | 0.018 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".