Electoral Candidate Debates for Policy Learning in Large First-Year Classes
Bibliographic record
Abstract
The benefits of experiential learning are well-documented, but large course enrollment can be seen as a barrier to providing meaningful experiential learning experiences. Political science literature on experiential learning in large undergraduate classes has prioritized simulations of political processes over direct student engagement in actual political processes. This multiple case study analyzes two in-class electoral candidate debates, one municipal and one federal, organized in a 300-student introductory social welfare course. Detailing the tensions inherent to organizing for maximum student engagement, and drawing on qualitative data from 73 student reflections, we found that in-class electoral candidate debates are feasible and effective as an experiential civic education activity. Though preparation work was complex and substantial, in-class candidate debates resulted in a rich learning foundation for the whole course. Key components for effective learning included student generated topics and questions and a wide range of candidates. Debriefing was also essential given the varied levels of prior knowledge inevitable in large classes. This paper extends the literature on teaching in the large policy classroom to a promising new experiential learning activity. It provides useful guidance for others who wish to harness the benefits of experiential civic education in large classes.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".