The Votes Are In! Candidate Debates as Large Policy Course Experiential Learning Method
Bibliographic record
Abstract
Background: Like other professional training programs, social work pedagogy has long recognized the value of experiential learning for professional development. Despite social work's rich experiential learning literature involving field education, direct practice courses, and program evaluation, there is a dearth of literature examining how to make learning in the policy classroom experiential, particularly for large class sizes. Purpose: We asked, “How might electoral candidate debates provide experiential learning opportunities for large classes?” Approach: The authors organized municipal and federal election candidate debates attended in-person and online by over 300 undergraduate students in a social work policy class at a Canadian university. Integrating our experiences as instructors/organizers and a teaching assistant, within a social constructivist framework, we used Kolb's experiential learning theory, and critiques thereof, to analyze reflective assignments from 73 students. Results and Conclusions: Candidate debates, when facilitated appropriately, can encourage students in large courses to work through the stages of experiential learning and consider related concepts and possible links among social justice course content and social policy, social work practice, and political engagement. Implications: The paper contributes to a broader understanding of the opportunities and constraints associated with employing experiential learning in the large social work classroom and beyond.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".