Promising practices and constraining factors in mobilizing community-engaged research
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 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.
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 | 0.456 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.060 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.090 |
| 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