Using Critical Race Theory to Analyse Community Engagement Practice in a Graduate Social Work Course
Bibliographic record
Abstract
Post-secondary institutions are increasingly encouraging partnership engagement with the community; however, community engagement from an academic perspective does not necessarily benefit the community. This is partially due to the power differential in this relationship and the emphasis on students’ learning at the community’s expense. The content of this article is drawn from experiences gleaned from 11 students of the “Perspectives with Diverse Communities” (institute component) course at Memorial University, Canada. Of the group, eight identified as cisgender, heterosexual, white females. The professor—a Black woman—and two students deviated from this in terms of gender identity, sexual orientation, and race. During a week of on-campus education, the students participated in community engagement activities prompted by the 2017 United States ban on immigration and refugees. Through a Critical Race Theory (CRT) lens, the students acknowledged their own identities as mostly white cisgender women, given the institutional racism surrounding them. As graduate students, they are taught self-reflexive practice, but question whether this is enough to effectively work with Black, Indigenous, and racialised groups. During the course institute, they steered towards a course of action that was familiar to them instead of developing deeper levels of understanding in working with Black, Indigenous, and racialised populations. This article details one aspect and the process of community engagement undertaken by the class and provides a critical reflection on how the students could have better engaged the community and challenged power dynamics and epistemology while using CRT.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.026 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".