MétaCan
Menu
Back to cohort
Record W3048245779 · doi:10.1111/1475-6765.12417

Policy responsiveness to all citizens or only to voters? A longitudinal analysis of policy responsiveness in OECD countries

2020· article· en· W3048245779 on OpenAlexafffund
Ruth Dassonneville, Fernando Feitosa, Marc Hooghe, Jennifer Oser

Bibliographic record

VenueEuropean Journal of Political Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIdeologyDemocracyRepresentation (politics)Mechanism (biology)Public opinionVoter turnoutConstruct (python library)TurnoutPolitical sciencePublic policyWelfarePublic economicsEconomicsPoliticsPolitical economyVotingLaw

Abstract

fetched live from OpenAlex

Abstract A close connection between public opinion and policy is considered a vital element of democracy. In representative systems, elections are assumed to play a role in realising such congruence. If those who participate in elections are not representative of the public at large, it follows that the reliance on elections as a mechanism of representation entails a risk of unequal representation. In this paper, we evaluate whether voters are better represented by means of an analysis of policy responsiveness to voters and citizens in democracies worldwide. We construct a uniquely comprehensive dataset that includes measures of citizens’ and voters’ ideological (left–right) positions, and data on welfare spending in Organisation for Economic Cooperation and Development countries since 1980. We find evidence of policy responsiveness to voters, but not to the public at large. Since additional tests suggest that the mechanism of electoral turnout does not cause this voter‐policy responsiveness, we outline alternate mechanisms to test in future research.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.234
GPT teacher head0.506
Teacher spread0.273 · 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 designObservational
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

Citations28
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Journal of Political ResearchSame topicElectoral Systems and Political ParticipationFrench-language works237,207