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Record W3132948749 · doi:10.1017/gov.2021.4

The Perception of the Legitimacy of Citizens’ Assemblies in Deeply Divided Places? Evidence of Public and Elite Opinion from Consociational Northern Ireland

2021· article· en· W3132948749 on OpenAlexfundno aff
John Garry, James Pow, John Coakley, David M. Farrell, Brendan O’Leary, James Tilley

Bibliographic record

VenueGovernment and Opposition · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen's University BelfastQueen's UniversityUniversity of Cambridge
KeywordsEliteLegitimacyIdeologyPublic opinionPolitical sciencePoliticsPerceptionPower (physics)Public relationsPublic administrationDeadlockPolitical economyLawSociologyPsychology

Abstract

fetched live from OpenAlex

Abstract How much public and elite support is there for the use of a citizens’ assembly – a random selection of citizens brought together to consider a policy issue – to tackle major, deadlock-inducing disagreements in deeply divided places with consociational political institutions? We focus on Northern Ireland and use evidence from a cross-sectional attitude survey, a survey-based experiment and elite interviews. We find that the general public support decision-making by a citizens’ assembly, even when the decision reached is one they personally disagree with. However, support is lower among those with strong ideological views. We also find that elected politicians oppose delegating decision-making power to an ‘undemocratic’ citizens’ assembly, but are more supportive of recommendation-making power. These findings highlight the potential for post-conflict consociations to be amended, with the consent of the parties, to include citizens’ assemblies that make recommendations but not binding policy.

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)0.017
metaresearch head score (Gemma)0.034
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0040.011
Scholarly communication0.0080.003
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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

Citations43
Published2021
Admission routes1
Has abstractyes

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