Creating Better Citizens? Effects of a Model Citizens' Assembly on Student Political Attitudes and Behavior
Bibliographic record
Abstract
Perceiving political engagement to be dangerously low among American citizens, many political science professors in recent years have attempted to promote engagement and “healthier” political attitudes. The effectiveness of these efforts appears variable and generally quite modest. Following the model of Canadian citizens' assemblies, we taught a course called Citizens' Assembly on Critical Thinking about the United States (CACTUS) in spring 2008 in which students considered the question: “Is it time to change the way we elect the President of the United States?” Because the course employs a form of deliberative democracy CACTUS might be anticipated to encourage engagement. We use a pre-post survey design to measure attitudes of both CACTUS (treatment group) and other (comparison groups) students to examine this. We find that both CACTUS and students enrolled in other political science courses experienced modest growth in their political engagement. More notably, CACTUS students became more extreme in their party identification, ideology, and issue positions and became more supportive of the existing electoral system. We suspect these findings are attributable to the nature and content of CACTUS. Our findings have important implications for future efforts to promote political engagement and for measuring the effects of those efforts.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".