Bibliographic record
Abstract
Over the last few decades, democratic theory has grown dramatically in its power and sophistication, fueled by debates among models of democracy. But these debates are increasingly unproductive. Model-based strategies encourage theorists to overgeneralize the place and functions of ideal typical features of democracy, such as deliberation or elections. Here I sketch an alternative strategy based on the question: What kinds of problems does a political system need to solve to count as “democratic”? I suggest three general kinds: it should empower inclusions, form collective agendas and wills, and have capacities to make collective decisions. We can view common practices such as voting and deliberating as means for addressing these problems, and theorize institutional mixes of practices that would maximize a political system's democratic problem-solving capacities. The resulting theories will be both normatively robust and sufficiently fine-grained to frame democratic problems, possibilities, and deficits in complex polities.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".