What Is It Like to Do Community-Engaged Research? Lessons Learned From University Researchers’ Perspectives
Bibliographic record
Abstract
Community-engaged research calls on us to rethink ourselves as researchers and to address lopsided researcher-researched relationships. As a group of university researchers, we participated in a research-practice partnership that included a research-intensive university, an internationally recognized professional learning network, a ministry of education funder, and a school district in Alberta, Canada. Despite the long-standing, collaborative relationships between these organizations, a spin-off research partnership slid into traditional research practices that limited the project’s potential. To critically reflect on these events, we engaged in eight cogenerative dialogues and three semistructured interviews to examine key moments in the partnership more closely. Our findings highlight how limitations in our fields of view as well as significant changes at crucial points in the partnership affected our ability to engage in sustained community-engaged research. We discuss critical learnings about this partnership in particular and offer recommendations that will help future research-practice partnerships assess and sustain their collaborations in meaningful ways.
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 | 0.160 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.059 |
| Scholarly communication | 0.037 | 0.033 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.016 | 0.027 |
| Insufficient payload (model declined to judge) | 0.002 | 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".