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Record W4412234378

Democracy, Populism, and Concentrated Interests

2023· article· en· W4412234378 on OpenAlexaff
Shai Dothan

Bibliographic record

VenueeYLS (Yale Law School) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsCentre for International Governance Innovation
Fundersnot available
KeywordsPopulismDemocracyPolitical sciencePolitical economyPublic administrationLaw and economicsLawSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

Concentrated interest groups have a significant advantage over diffuse interest groups: they can effectively stop free riding among their members. Because of this advantage, concentrated interest groups work in unison and manage to capture the government in many democracies. Democratic mechanisms of separation of powers, an independent judiciary, and the rule of law are designed to prevent the capture of government by concentrated interests. Under certain conditions, these mechanisms make it possible for diffuse interests to have a fair share of the influence over the government. Populist ideologists doubt that claim, however. They are convinced that democracies are captured by a small elite that controls most of the political power. The declared aim of populists is to give political power back to the majority of society. Despite that declared aim, this Article argues that the actions taken by populists have exactly the opposite outcome. By downgrading democratic mechanisms that constrain the government, populists end up making it easier for concentrated interests to capture the government and take advantage of diffuse groups.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.006
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.032
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.289
Teacher spread0.260 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

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