The Impact of Year-Long Deliberative Processes on the Structure and Quality of Opinions
Bibliographic record
Abstract
In three recent instances, politicians have let a group of ordinary people decide important public policy. Citizen assemblies on electoral reform were implemented in British Columbia, the Netherlands, and Ontario. Participants were selected through a combination of random draw and self-selection. They spent almost an entire year learning about electoral systems, consulting the public, deliberating, debating and ultimately deciding what design should be adopted. This paper examines the structure and quality of individual and collective decisions reached by citizens during these lengthy deliberations dealing with a complex, technical and unfamiliar issue. Specifically, we ask: To what extent are individual preferences driven by general political values and specific objectives of electoral reform in a logical and coherent way? Did the structure of opinion determinants evolve (increasing or decreasing) over the span of the proceedings? Are there important differences in the decisions made by the (initially) better informed as compared to the less knowledgeable? And were the final collective recommendations to the public and the government reasonable? In sum, this paper evaluates the capacity of citizens to overcome their typical political ignorance and to articulate well-reasoned policy proposals.
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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 | 0.032 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".