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Record W4399288688 · doi:10.1057/s41304-024-00489-2

The Pitkinian public: representation in the eyes of citizens

2024· article· en· W4399288688 on OpenAlexaff
Liron Lavi, Clareta Treger, Naama Rivlin-Angert, Tamir Sheafer, Israel Waismel-Manor, Shaul R. Shenhav, Liran Harsgor, Michal Shamir

Bibliographic record

VenueEuropean Political Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Toronto
FundersBar-Ilan University
KeywordsComparative politicsRepresentation (politics)Political philosophyPolitical scienceMulti-level governanceInternational relationsEuropean integrationPoliticsPublic administrationSociologyLawEuropean unionEconomics

Abstract

fetched live from OpenAlex

Abstract Democracy is backsliding in Europe and around the world as citizens’ trust in elected representatives and institutions wanes. Representation theories and studies have mostly centred on the representatives, rather than the represented. But how do citizens perceive political representation? Are their perceptions of any consequence at all? In this paper, we set forth a framework of representation in the eyes of citizens, based on Pitkin’s classic concept of representation in conjunction with Weissberg’s distinction between dyadic and collective representations. We use Israel as a proof of concept for our theoretical framework, employing an original set of survey items . We find that, in keeping with Pitkin’s framework, citizens perceive representation as multidimensional and depreciate the descriptive and symbolic—the standing-for—dimensions. Furthermore, citizens’ democratic attitudes are shaped by collective representation by the parliament rather than by dyadic representation by an elected representative. We conclude with a call for a greater focus on representation from the citizens’ standpoint.

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)0.007
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.013
Scholarly communication0.0050.004
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.073
GPT teacher head0.392
Teacher spread0.319 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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