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New Political Governance in Westminster Systems: Impartial Public Administration and Management Performance at Risk

2012· article· en· W2126042511 on OpenAlexaffabout
Peter Aucoin

Bibliographic record

VenueGovernance · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPoliticsPublic administrationStaffingTransparency (behavior)Corporate governancePublic serviceAuditNew public managementPublic relationsAdministration (probate law)BusinessPolitical scienceAccountingLawPublic sectorFinance

Abstract

fetched live from OpenAlex

This article examines the phenomenon of increased political pressures on governments in four Westminster systems (Australia, Britain, Canada, and New Zealand) derived from changes in mass media and communications, increased transparency, expanded audit, increased competition in the political marketplace, and political polarization in the electorate. These pressures raise the risk to impartial public administration and management performance to the extent that governments integrate governance and campaigning, allow political staff to be a separate force in governance, politicize top public service posts, and expect public servants to be promiscuously partisan. The article concludes that New Zealand is best positioned to cope with these risks, in part because of its process for independently staffing its top public service posts. The article recommends this approach as well as the establishment of independently appointed management boards for public service departments and agencies to perform the governance of management function.

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)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.014
Scholarly communication0.0130.005
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.037
GPT teacher head0.341
Teacher spread0.304 · 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 designTheoretical or conceptual
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

Citations285
Published2012
Admission routes2
Has abstractyes

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