Building a Better Referendum: Linking Mini-Publics and Mass Publics in Popular Votes
Bibliographic record
Abstract
Popular votes and mini-publics are both increasingly implemented as elected officials seek to build legitimacy for decisions, although these democratic innovations suffer from their own democratic deficits. Popular votes often do not live up to deliberative ideals while mini-publics may be limited in their capacities for inclusion and decision-making. Pairing these two devices can improve deliberation in referendum campaigns, while tying mini-publics to a clear and inclusive process for decision-making. Empirical studies of this strategy have found both successes and shortcomings. Little attention has been given to the possibility that the success of mini-publics in influencing public opinion is determined, in part, by the underlying design of the popular vote process. I outline how multi-stage popular votes could institutionalize an iterated dialogue between the micro-level mini-public and the mass, voting public to produce distinct democratic benefits. This serves as a model of how a systems approach to democratic theory can guide institutional design to address democratic functions of empowered inclusion, collective agenda and will formation, and collective decision-making.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".