Electoral Candidate Debates for Policy Learning in Large First-Year Classes
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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