When, Where and Why Do We Need Deliberation, Voting, and Other Means of Organizing Democracy? A Problem-Based Approach to Democratic Systems
Bibliographic record
Abstract
Over the last two decades, democratic theory has grown dramatically in its power and sophistication. A central feature of these developments has been a robust debate between advocates and critics of deliberative democracy. But the debates are less productive than they should be. The reasons often have less to do with substantive claims and evidence, than with a model-based style of thinking about the roles of deliberation, voting, and other means organizing democracy into a political system. This paper is sketches an alternative way of thinking democratic systems, such that we might understand the place of the means and mechanisms we have to organize democracy into political systems. The guiding intuition is simple and straightforward: different political means and mechanisms — deliberation among them — have problem-specific strengths within democratic systems. We can theorize these problems as functional requirements of democratic systems, look at the available means for serving these functions, and then judge the mixes of means that would maximize their (systematic) democratic effects. More specifically, I proceed proceeds by framing two kinds of questions: (1) What does a political system needs to accomplish to function “democratically”? What problems does it need to solve? I suggest that there are three broad functions we need to conceptualize, which I call empowered inclusion, communication and collective will-formation, and collective decision capacity. (2) What kinds of means do political systems have to accomplish these functions? I suggest that there is a limited number of generic means: voting, association (including resistance and advocacy), deliberation, consensus, and market-like competition. Each has strengths and weaknesses with respect to the three democracy-defining problems. Ideally, political systems should use (and institutionalize) these means in ways that maximize their strengths and minimize their weaknesses with respect to each of the three democracy-defining functions.
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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 | 0.036 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.062 |
| Scholarly communication | 0.027 | 0.052 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".