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Record W3127813979

Chapter 1 Theories of local power and multi-level conflict

2017· book-chapter· en· W3127813979 on OpenAlexaboutno aff
Ken Victor Leonard Hijino

Bibliographic record

VenueDirectory of Open access Books (OAPEN Foundation) · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPower (physics)Political sciencePhysicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

This book is about why and how central and local governments clash over important national policy decisions. Its empirical focus is on the local politics of Japan which has significantly shaped, and been shaped by, larger developments in national politics. The book argues that since the 1990s, changes in the national political arena, fiscal and administrative decentralization, as well as broader socio-economic developments have led to a decoupling of once closely integrated national and local party systems in Japan. Such decoupling has led to a breakdown of symbiotic relations between the centre and regions. In its place are increasing strains between national and local governments leading to greater intra-party conflict, inter-governmental conflicts, and more chief executives with agendas and resources increasingly autonomous of the national ruling party. Although being a book primarily focused on the Japanese case, the study seeks to contribute to a broader understanding of how local partisans shape national policy-making. The book theorizes and investigates how the degree of state centralization, vertical integration for party organizations, and partisan congruence in different levels of government affect inter-governmental relations. Japan’s experience is compared with Germany, Canada, and the UK to explore sources of multi-level policy conflict.

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, Scholarly communication, 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 score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.160
GPT teacher head0.427
Teacher spread0.266 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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