Engaging Students in Social Policy and Social Justice: The Use of Candidate Debates in Canadian BSW Education
Bibliographic record
Abstract
Social work pedagogy recognizes the educational value of experiential learning for the professional development of social workers, with a particularly rich experiential learning literature related to clinical work and field education. This study evaluates an experiential learning activity for large undergraduate courses in another area: social policy and social justice. We ask: How effective are electoral candidate debates in building BSW students’ understanding of social justice and its relationship with social policy? Using a constructionist approach, we qualitatively analyzed reflection data from 73 students on their experiences of two in-class electoral candidate debates (one municipal, one federal) held in consecutive offerings of a first-year survey course. Findings indicate that in-class electoral debates have the potential to effectively support learning and engagement related to social policy and social justice, especially foundational concepts such as Canadian federalism, ideologies that inform policy responses, and equity analysis of different policy responses. Learning was primarily limited to formalized conceptualizations of social justice. Recommendations to maximize learning include assessment and accommodation of the diversity of prior student knowledge and inclusion of briefing and debriefing activities. The study suggests that in-class electoral debates, if done properly, can be an effective experiential teaching tool for policy courses.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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 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".