“Community First” for Whom? Reflections on the Possibilities and Challenges of Community-Campus Engagement from the Community Food Sovereignty Hub
Bibliographic record
Abstract
While community-campus engagement (CCE) has gained prominence in postsecondary institutions, critics have called for a more direct focus on community goals and objectives. In this paper, we explore the possibilities and limitations of community-centred research through our collective experiences with the Community First: Impacts of Community Engagement (CFICE) and the Community Food Sovereignty (CFS) Hub. Drawing on a four-year research project with twelve community-campus partnership projects across Canada, we outline three key areas for reflection. First, we examine the meanings of community-centred research—called “community first”—in our work. Second, we explore key tensions that resulted from putting “community first” research into practice. Third, we discuss possibilities that emerged from attempts to engage in “community first” CCE. We suggest that while putting “community first” presents an opportunity to challenge hierarchical relationships between academia, western ways of knowing, and community, it does not do so inherently. Rather, the CCE process is complex and contested, and in practice it often fails to meaningfully dismantle hierarchies and structures that limit grassroots community leadership and impact. Overall, we argue for the need to both champion and problematize “community first” approaches to CCE and through these critical, and sometimes difficult conversations, we aim to promote more respectful and reciprocal CCE that works towards putting “community first.”
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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 | 0.053 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.057 | 0.089 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.009 | 0.023 |
| 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".