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Prime Ministers

2018· book· en· W4238831954 on OpenAlexaboutno aff
Patrick Weller

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPrime (order theory)Council of MinistersNormativeScope (computer science)Power (physics)Political sciencePoliticsPublic administrationLawComputer scienceEconomicsMathematicsInternational tradeEuropean union

Abstract

fetched live from OpenAlex

Prime ministers dominate, but still lose office. Why? This chapter explores the conundrum that infests debates on the prime ministers’ power and influence. It examines the ambiguous and uncertain institutional environment with its scope for initiatives and choices. It rejects normative notions of what prime ministers should do and illustrates that accusations of excessive power and ambition have been thrown at prime ministers from the very beginning. It identifies the strategy of the book: to compare institutional arrangements in four Westminster systems—Britain, Australia, Canada, and New Zealand—to see how prime ministers have chosen to define their role. It proposes tests for assessing prime ministers and for judging their performance and options.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0710.038

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 head0.028
GPT teacher head0.246
Teacher spread0.218 · 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 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
Published2018
Admission routes1
Has abstractyes

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