8 A problem-based approach to citizens’ assemblies
Bibliographic record
Abstract
Growing concerns with legitimacy deficits in democracies are motiving interest in CAs. These processes can address some of these deficits quite well, especially, citizen representation and considered deliberation of issues by non-elites. However, CAs are not the solution to every problem of democratic legitimacy. How should we understand their strengths and weaknesses within democratic systems? We address these questions by drawing on a problem-based approach to democracy. To count as a democratic, a political system must empower inclusions, form popular collective wills and agendas, and make decisions that provide collective goods, such that a people is acting on its own behalf. This framework allows us to theorize the contributions of CAs within the context of democratic systems. We examine seven sites of potential contributions: elections, ballot measures, legislatures, executive agencies, public spheres, political parties, and constitutional processes. This approach specifies and calibrates our expectations for CAs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".