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Record W2891564017 · doi:10.1017/s0003055418000527

The Power of the Multitude: Answering Epistemic Challenges to Democracy

2018· article· en· W2891564017 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Political Science Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultitudeDemocracyWritElitePolitical sciencePoliticsLaw and economicsEpistemologyCompetence (human resources)SociologyPositive economicsLawEconomicsPhilosophyManagement

Abstract

fetched live from OpenAlex

Recent years have witnessed growing controversy over the “wisdom of the multitude.” As epistemic critics drawing on vast empirical evidence have cast doubt on the political competence of ordinary citizens, epistemic democrats have offered a defense of democracy grounded largely in analogies and formal results. So far, I argue, the critics have been more convincing. Nevertheless, democracy can be defended on instrumental grounds, and this article demonstrates an alternative approach. Instead of implausibly upholding the epistemic reliability of average voters, I observe that competitive elections, universal suffrage, and discretionary state power disable certain potent mechanisms of elite entrenchment. By reserving particular forms of power for the multitude of ordinary citizens, they make democratic states more resistant to dangerous forms of capture than non-democratic alternatives. My approach thus offers a robust defense of electoral democracy, yet cautions against expecting too much from it—motivating a thicker conception of democracy , writ large.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.022
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.063
GPT teacher head0.407
Teacher spread0.344 · 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