Harms and possibilities: Social work doctoral students reflect on social justice pedagogy
Bibliographic record
Abstract
Co-written by four doctoral students and a professor, this article reflects on a novel social work doctoral seminar, “Social Justice Pedagogy.” This course was offered in the 2022 fall term at Wilfrid Laurier University in Ontario, Canada, as part of concerted efforts to acknowledge learning spaces as sites of harm. The mutual draw to this course included all of the authors witnessing, causing, experiencing, or fearing harm in the classroom. This course became about the process and experience of a socially just pedagogical approach. Few PhD social work programs require or even offer a course on the discipline-specific skills of teaching social justice content (Lee et al., 2022; Oktay et al., 2013; Pryce et al., 2011). In this paper, we discuss our motivations for engaging in this novel course and share key insights gained through challenging discussions of racism, gender, and neurodiversity. We engage with themes of intersubjectivity and intersectional identities, attending to process and content in social work education. Further, we engage possibilities related to creating and fostering spaces that emphasize non-hierarchical communication and learning to shape a community of practice among educators. This reflects and reinforces the expectations of social workers to routinely reflect on ethical dilemmas, questions, challenges, and relational dynamics.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.039 | 0.041 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.002 | 0.042 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".