When Citizens Decide
Bibliographic record
Abstract
Three unprecedented large-scale democratic experiments have recently taken place. Citizen assemblies on electoral reform were conducted in British Columbia, the Netherlands, and Ontario. Groups of randomly selected citizens were asked to design the next electoral system. In each case, the participants spent almost an entire year learning about electoral systems, consulting the public, deliberating, debating, and ultimately deciding what specific institution should be adopted. In this book, these unique cases are used to examine claims about citizens’ capacity for democratic deliberation and active engagement in policymaking. Empirical insight is offered to numerous debates: Are ordinary citizens able to decide about a complex issue? Are their decisions reasonable? Who takes part in such proceedings? Are they dominated by people dissatisfied by the status quo? Do some citizens play a more prominent role than others? Are decisions driven by the most vocal or most informed members? Did the participants decide by themselves? Were they influenced by staff, political parties, interest groups, or the public hearings? Does participation in a deliberative process foster citizenship? Did participants become more trusting, tolerant, open-minded, civic-minded, interested in politics, and active in politics? How do the other political actors react? Can the electorate accept policy proposals made by a group of ordinary citizens? The lessons drawn from this research are relevant for those interested in political participation, public opinion, deliberation, public policy, and democracy.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".