Graduate Students, Community Partner, and Faculty Reflect on Critical Community Engaged Scholarship and Gender Based Violence
Bibliographic record
Abstract
This article reflects on the challenges and opportunities associated with community engaged learning at the graduate level, and challenges higher education to do more to support the teaching–research–service nexus. The community university partnership involved a graduate student class, a faculty member, and a community member from a provincial not for profit association. We examined our principled and collaborative process of critical community engaged scholarship geared toward addressing violence against women, and more specifically, femicide. Our research resulted in knowledge mobilization tools that could be used to inform various audiences (e.g., women’s shelter staff, the public, government, and journalists) about how mainstream media sources report and portray the issue of femicide. Our work had an explicit social justice focus with aims to generate a better understanding of the structural causes of violence against women and historically-created gendered hierarchy and its ongoing impacts. This paper offers insights for others interested in pursuing community engaged research within a community engaged learning environment.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.033 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.002 | 0.036 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".