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Record W2513710671 · doi:10.1111/gove.12224

Promiscuously Partisan? Public Service Impartiality and Responsiveness in Westminster Systems

2016· article· en· W2513710671 on OpenAlexaboutno aff
Dennis Grube, Cosmo Howard

Bibliographic record

VenueGovernance · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsnot available
FundersAustralian Research CouncilGriffith University
KeywordsImpartialityTreasuryPublic administrationPoliticsGovernment (linguistics)Agency (philosophy)Political sciencePublic serviceLimelightLawSociologyPublic relationsSocial science

Abstract

fetched live from OpenAlex

Public servants in Westminster countries are being drawn into the limelight by demands from their political masters that they publicly defend policies. Critics suggest these conditions undermine the capacity and willingness of senior public servants to manage the enduring Westminster tension between serving elected governments and remaining nonpartisan. Interviews with senior officials from Australia, Canada, and the United Kingdom challenge this pessimistic view, showing that officials consistently stress the importance of not “crossing the line” when dealing with their elected masters. Two exploratory case studies are presented—one of an Australian ministerial department (Treasury) and another of a Canadian quasi‐autonomous agency (Statistics Canada)—in which public servants faced pressure to defend controversial government policies. These cases show how contemporary public servants actively interpret, establish, and defend the line between appropriate responsiveness and inappropriate partisanship in Westminster systems.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.135
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.026
Scholarly communication0.0110.004
Open science0.0010.006
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 head0.041
GPT teacher head0.274
Teacher spread0.233 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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