Promising practices and constraining factors in mobilizing community-engaged research
Bibliographic record
Abstract
This article describes a project involving 13 community focus groups on the topic of anti-racism and belonging where the researchers concluded each group with a robust discussion about how the group would prefer to receive the findings from the project. Analysis of this data, existing literature, and the practical experiences of the researchers revealed that while there are multiple “bridges” researchers can take to connect their research with community-level users, and although it is desirable to offer tailored approaches for specific audiences, there are significant barriers and challenges for truly effective engagement. By describing the various factors that determined which bridges were taken, we hope to help other community-based researchers imagine new ways of mobilizing knowledge, consider promising practices to guide the connection of knowledge to the community and shine a light on the very real constraints of time, budget, personnel, and university system considerations that impact knowledge mobilization decisions.
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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 | 0.175 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.029 | 0.050 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".