Educating for Justice: Challenges and openings at the beginning of a new century
Bibliographic record
Abstract
Educating for social justice is integral to social work’s mandate. Yet too often consciousness-raising does not lead to an engaged praxis beyond the classroom. This article explores the link between education and action and discusses some current challenges and openings towards a more committed and integrated social work practice. It draws on an experience of doing social action with social work students and faculty at the Quebec Summit of the Americas in April, 2001. I begin by situating my comments contextually, for my approach to social work comes from a particular orientation to practice. I then highlight some core features about the Quebec experience and some reflections on our learning and acting together. Insights from this experience have led me to a deeper questioning about the nature of social work at the beginning of this new century and have left me wondering about what kind of practice and what kind of world we are preparing students for. In the last section of the article, aided by reflections from Rolland Smith (1996), I identify some current challenges and suggest some potential openings from which to rethink what we are doing in terms of a more engaged praxis as teachers and practitioners.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.036 | 0.068 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".