The how of social justice education in social work: Decentering colonial whiteness and building relational reflexivity through circle pedagogy and Image Theatre
Bibliographic record
Abstract
Teaching courses on social justice are mainstays of social work education and are considered imperative for ethically responsible social work practice. Social justice education comes with many challenges, including white settler student resistance and difficulties translating social justice content into social work practice. We suggest a move from viewing social justice practice as a value or skill set - as part of the ‘professional self’ – to one that understands social justice as relational and as a politics of being and acting. In this article, we discuss methods for decentering colonial whiteness in the social work classroom by adopting relational reflexive pedagogies more congruent with social justice content. The first half of the article focuses on the white and colonial epistemological foundations of social justice education in social work and notions of the social worker subjectivity as ‘good’ and ‘moral’. In the second half of the article, we invite social work educators to reflect on congruency between social justice theories discussed in the classroom and the practice of how we teach these concepts. We then offer circle pedagogy and Image Theatre exercises as examples of practicing a relational ethic and politic of social justice in the process of teaching.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".