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Record W2590968381 · doi:10.1093/pa/gsx002

Constituency Pressures on Committee Selection: Evidence from the Northern Ireland Assembly and Dáil Éireann

2017· article· en· W2590968381 on OpenAlexfundno aff
Christopher Raymond, Jacob Holt

Bibliographic record

VenueParliamentary Affairs · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsLegislatureSelection (genetic algorithm)Political scienceOrder (exchange)Public relationsPublic administrationPrimary electionLawGeneral electionBusinessPoliticsComputer scienceFinance

Abstract

fetched live from OpenAlex

Most previous research examining selection to committees assumes constituency pressures—leading representatives to seek committee assignments dealing with their constituents’ particularistic interests that improve their re-election prospects—are incompatible with disciplined parties, which may prevent such personal vote-seeking behaviour in order to preserve the party’s brand. In contrast, we argue parties will support committee assignments promoting members’ re-election chances because parties benefit from their members’ re-election. Analysing two legislatures with highly disciplined parties and electoral systems encouraging personal vote-seeking—the Northern Ireland Assembly and Dáil Éireann—our analysis suggests constituency pressures increase the chances of selection to committees enhancing members’ re-election prospects.

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)0.024
metaresearch head score (Gemma)0.063
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 score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.004
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.057
GPT teacher head0.339
Teacher spread0.282 · 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

Citations35
Published2017
Admission routes1
Has abstractyes

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