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Record W4387743897 · doi:10.1007/s11158-023-09635-x

Why Deliberation and Voting Belong Together

2023· article· en· W4387743897 on OpenAlexaff
Simone Chambers, Mark E. Warren

Bibliographic record

VenueRes Publica · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDeliberationVotingPolitical scienceLaw and economicsPoliticsDeliberative democracyReferendumDemocracyPublic relationsSociologyLaw

Abstract

fetched live from OpenAlex

Abstract The field of deliberative democracy now generally recognizes the co-dependence of deliberation and voting. The field tends to emphasize what deliberation accomplishes for vote-based decisions. In this paper, we reverse this now common view to ask: In what ways does voting benefit deliberation? We discuss seven ways voting can complement and sometimes enhance deliberation. First, voting furnishes deliberation with a feasible and fair closure mechanism. Second, the power to vote implies equal recognition and status, both morally and strategically, which is a condition of democratic deliberation. Third, voting politicizes deliberation by injecting the strategic features of politics into deliberation—effectively internalizing conflict into deliberative processes, without which they can become detached from their political environments. Fourth, anticipation of voting may induce authenticity by revealing preferences, as what one says will count. Fifth, voting preserves expressions of dissent, helping to push back against socially induced pressures for consensus. Sixth, voting defines the issues, such that deliberation is focused, and thus more likely to be effective. And, seventh, within contexts where votes are public—as in representative contexts, voting can induce accountability, particularly for one’s claims. We then use these points to discuss four general types of institutions—general elections, legislatures, minipublics, and minipublics embedded in referendum processes—that combine talking and voting, with the aim of identifying designs that do a better or worse job of capitalizing upon the strengths of each.

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 imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.059
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.034
Scholarly communication0.0120.018
Open science0.0020.008
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.040
GPT teacher head0.336
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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