Decolonizing Social Work Education and Practice with Students and Community Stakeholders: A Case Example from University of Calgary
Bibliographic record
Abstract
Social work education in Canada has struggled to identify and disrupt systems of oppression and marginalization, rooted in a history of colonialism and slavery, that continues to reverberate through neocolonial policies, pedagogy, and practice. Universities and social work programs remain white spaces that uphold white supremacy, while actively excluding nonwhite members and their ways of knowing and being in the world. It wasn’t until the deepened retrenchment of anti-Black racism during the pandemic, punctuated by the killing of George Floyd, that a group of educators seized an opening to teach an anti-racist and anti-colonial praxis course for the Bachelor of Social Work students in spring of 2021, the first one of its kind in our faculty. Students were encouraged to engage with Indigenous and racialized community youth to imagine and integrate anti-racist and reconciliatory actions as part of their assignments. This paper shares our experiences co-designing and co-teaching this course together with community partners. We emphasize lessons from the applied pedagogical approaches as well as its implications for social work education and social work practice.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.077 | 0.030 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".