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Record W4362548804 · doi:10.1093/pa/gsad005

Ministerial Advisers as Power Resources: Exploring Expansion, Stability and Contraction in Westminster Ministers’ Offices

2023· article· en· W4362548804 on OpenAlexaffabout
Heath Pickering, Jonathan Craft, Marleen Brans

Bibliographic record

VenueParliamentary Affairs · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTypologyPoliticsNarrativePublic administrationGovernment (linguistics)Proxy (statistics)SociologyPolitical stabilityPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract In this article, we argue that the entourage of ministerial advisers available to prime ministers and other ministers is an institutional power resource that can serve as a useful indicator to measure the changing nature of the political executive. Two novel contributions are made utilising four new datasets on ministerial advisers coupled with a comparative analysis of 21 governments in Australia, Britain, Canada and New Zealand, in varying dates between 1997 and 2020. First, by using ministerial advisers as a proxy indicator, we chart how the offices of executive politicians can either expand, remain stable or contract. As a corrective to the general long-term narrative that ministers’ offices continually expand, our evidence shows this expansion has in some cases been interrupted and more generally manifests in different patterns from one government to the next. Second, we interrogate these patterns against the background of four typical assumptions from the party family, government tenure, parliamentary control and leadership stability literature. The new datasets, typology and analysis provide fresh comparative insights to advance our understanding about the evolving nature of the political executive in the four classic Westminster family countries.

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)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.094
GPT teacher head0.354
Teacher spread0.259 · 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 designQualitative
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

Citations10
Published2023
Admission routes2
Has abstractyes

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