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Record W1510410500 · doi:10.21427/d7mg78

The Extent of Clientelism in Irish Politics: Evidence from Classifying Dáil Questions on a Local-National Dimension

2010· article· en· W1510410500 on OpenAlex
Sarah Jane Delany

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArrow@dit (Dublin Institute of Technology) · 2010
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
FundersScience Foundation IrelandCanada Millennium Scholarship Foundation
KeywordsIrishClientelismPoliticsDimension (graph theory)Representation (politics)Proportional representationPolitical scienceLawLinguistics

Abstract

fetched live from OpenAlex

The availability of the full text of Irish parliamentary questions offers opportunities for using machine learning techniques to examine the currently much discussed role of elected representatives (TDs) in the Irish parliamentary system. Bluntly, are TDs mainly national legislators or “constituency messenger boys”? This paper presents an initial investigation into the use of automated text classification techniques to categorise parliamentary questions from 1922 up to 2008 as national or local. The approach uses a bag of words representation, standard feature reduction methods and an SVM classifier. Initial results show there is very little evidence in the corpus of parliamentary questions in Dail Eireann to support the view that the role of the TD is determined by mainly clientelist/parochial imperatives.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.306
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