Bibliographic record
Abstract
This article addresses the following question: Does the theory of deliberative democracy have any place in the electoral process? Decision-making through deliberation is considered to be a central value in a democracy. However, critics charge that deliberation is highly unlikely to take place during elections or in politics more generally. This article intervenes in this debate in two respects. First, it argues for the concept of second-best deliberation. Under a second-best approach, deliberation is viewed as being context-specific: that is, the norms of deliberation change depending on the actors and institutions involved. I also argue that deliberation, instead of being treated as a unified theory, should be reconceived as existing along a spectrum. Second, this article uses the concept of second-best deliberation to evaluate the deliberative possibilities within and the shortcomings of a wide range of topics in U.S. election law, including electoral redistricting, majority-minority districts, political parties and partisanship, the Voting Rights Act, campaign finance regulation, election administration, and electoral reform. Although this article focuses on U.S. election law, the concept of second-best deliberation can be applied more broadly to evaluate the deliberative strengths and limitations of other democratic systems. The article also provides an extensive discussion of the theory of deliberative democracy and its various critiques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".