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Record W4380233583 · doi:10.1515/9780228013426

Government

2022· book· en· W4380233583 on OpenAlexaboutno aff
Donald J. Savoie

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2022
Typebook
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)BusinessPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Citizens have lost trust in their institutions of public governance. In trying to fix the problem, presidents and prime ministers have misdiagnosed the patient, failing to recognize that government bureaucracies are inseparable from political institutions. As a result, career officials have become adroit at managing the blame game but much less so at embracing change. Donald Savoie looks to the United States, Great Britain, France, and Canada to assess two of the most important challenges confronting governments throughout the Western world: the concentration of political power and the changing role of government bureaucracy. The four countries have distinct institutions shaped by distinct histories, but what they have in common is a professional non-partisan civil service. When presidents and prime ministers decide to expand their personal authority, national institutions must adjust while bureaucracies grow to fill the gap, paradoxically further constricting government efficacy. The side effects are universal – political power is increasingly centralized; Parliament, Congress, and the National Assembly have been weakened; Cabinet has lost standing; political parties have been debased; and civil services have been knocked off their moorings. Reduced responsibility and increased transparency make civil servants slow to take risks and politicians quick to point fingers. Government astutely diagnoses the problem of declining trust in government: presidents and prime ministers have failed to see that efficacy in government is tied to well-performing institutions.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.285
Teacher spread0.251 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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