Anti-Oppressive Education and the Trap of 'Good' Intentions: Lessons From an Interdisciplinary Workshop
Bibliographic record
Abstract
This article is about the challenges of doing anti-oppressive education in a post-secondary context with a community of interdisciplinary colleagues. Critical examination of an anti-oppressive workshop, and subsequent focus group, reveals how good intentions can nevertheless reproduce the conditions we seek to challenge. To make sense of the challenges of doing this work, the authors offer three different analyses of the feedback received in the focus group. Drawing on literature from anti-oppressive education, feminist and critical theory, the authors focus on the role of engagement and recognition to highlight the power that underlies participation. By analyzing key insights from a focus group discussion, the authors seek to disrupt the relationship between identities rooted in goodness and efforts to do anti-oppressive work. Anti-oppressive work must grapple with the lived political, everyday realities we inhabit. This necessarily involves actively challenging the structural conditions that facilitate oppression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".