Engaging Students in Social Policy and Social Justice: The Use of Candidate Debates in Canadian BSW Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it