“I had a big revelation”: Student Experiences in Community- First Community-Campus Engagement
Bibliographic record
Abstract
While there is a wealth of literature on community-campus engagement (CCE) that incorporates student perspectives from course-based community service learning settings, the stories of students involved in longer-term CCE projects remain underexplored. This paper addresses this gap by examining the experiences of students working as research assistants (RAs) within a multi-year Canadian CCE project, “Community First: Impacts of Community Engagement” (CFICE). Drawing on interviews with RAs, student insights from a general evaluation of the CFICE project, and the authors’ own reflections, we consider the ways in which meaningful, long-standing engagements with community partners as part of community-first CCE projects provide students with both enhanced opportunities and challenges as they navigate the complexities of intersecting academic and community worlds. Further, this paper identifies promising practices to improve student experiences and the overall impact of longer-term community-campus partnerships and program management structures.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.028 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.006 | 0.015 |
| 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".