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Record W2588716391 · doi:10.1111/padm.12290

Temporary partisans, tagged officers or impartial professionals: Moving between ministerial offices and departments

2017· article· en· W2588716391 on OpenAlexaboutno aff
Maria Maley

Bibliographic record

VenuePublic Administration · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsImpartialityCompetence (human resources)PoliticsPublic administrationPublic servicePublic relationsPolitical scienceNorm (philosophy)Government (linguistics)SociologyLawManagementEconomics

Abstract

fetched live from OpenAlex

Abstract There are increasing concerns that the line between political and public service roles is becoming blurred, and that political advisers may be politicizing the work of public servants. Underlying this is the fundamental value conflict between responsiveness and impartiality and the challenge of balancing neutral competence and responsive competence in government. In Australia and Canada the norm of impartiality is challenged by the movement of staff between partisan ministers' offices and the public service. This is a case study comparison of how the risks posed by these transitions are managed through institutional rules and practices. This study of rule‐building by two countries with similar political institutions and shared traditions demonstrates the critical role played by rules which regulate activity between two organizations with opposed values. It finds differences of approach and attention in Canada and Australia and provides a lens through which to explore the contested boundaries of impartiality.

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)0.005
metaresearch head score (Gemma)0.014
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.136
Threshold uncertainty score0.270

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.011
Scholarly communication0.0080.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.154
GPT teacher head0.465
Teacher spread0.311 · 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

Citations44
Published2017
Admission routes1
Has abstractyes

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