Building Action Research Partnerships for Community Impact: Lessons From a National Community-Campus Engagement Project
Bibliographic record
Abstract
While many studies have addressed the successes and challenges of participatory action research, few have documented how community campus engagement (CCE) works and how partnerships can be designed for strong community impact. This paper responds to increasing calls for ‘community first’ approaches to CCE. Our analysis draws on experiences and research from Community First: Impacts of Community Engagement (CFICE), a collaborative action research project that ran from 2012-2020 in Canada and aimed to better understand how community-campus partnerships might be designed and implemented to maximize the value for community-based organizations. As five of the project’s co-leads, we reflect on our experiences, drawing on research and practice in three of CFICE’s thematic hubs (food sovereignty, poverty reduction, and community environmental sustainability) to identify achievements and articulate preliminary lessons about how to build stronger and more meaningful relationships. We identify the need to: strive towards equitable and mutually beneficial partnerships; work with boundary spanners from both the academy and civil society to facilitate such relationships; be transparent and self-reflexive about power differentials; and look continuously for ways to mitigate inequities.
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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 | 0.043 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".