From Turmoil to Transformation: Advancing Anti-Oppressive Social Work Education in an Era of Instability
Bibliographic record
Abstract
Anti-oppression is woven throughout the Council on Social Work Education (CSWE) 2022 Educational Policy and Accreditation Standards (EPAS). The call to action is clear; how to engage can be challenging. How do we bring anti-oppressive practice (AOP) pedagogy alive in the classroom? This article aims to create space in which Social Work educators can join us in reflecting upon and interrogating our collective practices as we seek to answer the call of our guiding educational standards and ethics while also weighing the critiques. The classroom represents a place of powerful parallel process, wherein Social Work educators can model many of the same concepts and skills we hope to impart to our students. Building from the tradition of story-telling as a form of knowledge and deconstruction, we, the authors, draw upon our theoretical perspectives, clinical and educational experiences, and lessons learned from our processes of bringing AOP pedagogy into our classrooms. We offer a community of ideas and collective commitment to the conviction that through AOP and AOP pedagogy, social workers can work together to create a more just society, one which honors the dignity and worth of all individuals, fostering human rights and the opportunity for all individuals to thrive.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.030 | 0.063 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.040 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".