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Record W2088319234 · doi:10.1111/npqu.11470

Post‐Party Democracy Can Restore the Rule of the Many Over Money

2014· article· en· W2088319234 on OpenAlexaff
Nicolas Berggruen, Nathan Gardels

Bibliographic record

VenueNew Perspectives Quarterly · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsGridlockDemocracyContext (archaeology)LegislatureCorporate governanceSeparation of powersPower (physics)Political scienceAccountabilityLaw and economicsPolitical economyEconomicsPublic administrationLawPoliticsManagement

Abstract

fetched live from OpenAlex

Historically, liberal democracy was born as a means to curb the power of kings and tyrants through mechanisms that would ensure accountability and consent of the governed. A system of checks and balances—two legislative chambers, executive and independent courts—were instituted to ensure power did not become too concentrated. Today's highly diverse, mass consumer societies, however, have presented another set of challenges. Power is so diffused governance is becoming ineffective. The short‐term mentality of voters and the lobbying of special interests undermine the ability of democracies to focus on the long‐term and the common interest. Because there are more checks than balances, gridlock has supplanted consensus. In this section, we compare Chinese and Western systems on their ability to deliver good governance. The editors of the Economist magazine put the debate in historical context.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0070.006
Open science0.0000.005
Research integrity0.0020.003
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 head0.011
GPT teacher head0.215
Teacher spread0.204 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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