Changing the Air: Strategies for Resisting and Decolonizing Social Work Education
Bibliographic record
Abstract
Despite social work’s dedication to social justice, social work education continues to uphold colonial practices and white supremacy. A growing body of literature addresses avenues to decolonize the social work classroom from the perspective of educators, but limited studies examine decolonizing the classroom from a student perspective. This paper, inspired by the concept of Métissage braiding, which embraces multiplicity of voice, offers critical reflections on decolonizing social work education from the perspectives of seven differently situated PhD cohort members in their first year of studies at the University of Calgary, Faculty of Social Work. From each student’s reflection, a unique perspective emerged, which culminated into seven themes of colonial resistance: changing the air, “whatever affects one affects all,” “I am because we are,” weaving an alternative fabric, air we can actually breathe, creating fugitive spaces through relationality, and decolonial worldmaking. These unique insights serve as a map for future students to navigate academic lives in colonial institutions. We recommend a shift in the colonial curriculum and proactive involvement of social work educators, students, researchers, and partners in engaging in decolonization practices to promote an inclusive and equitable society which serves as a catalyst for systematic change.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.059 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.010 |
| 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".