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Record W2152877979 · doi:10.1080/13597566.2010.507401

How Centralized Federations Avoid Over-centralization

2011· article· en· W2152877979 on OpenAlexaboutno aff
Dietmar Braun

Bibliographic record

VenueRegional & Federal Studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsFederalismDecentralizationCentralized governmentGovernment (linguistics)Central governmentPoint (geometry)Cooperative federalismDevolution (biology)Public administrationPolitical scienceLocal governmentEconomic systemBusinessEconomicsMarket economyPoliticsGeographyLaw

Abstract

fetched live from OpenAlex

The focus of this article is centralized types of federations that have been neglected both in the economic literature on federalism and in comparative federal studies. The starting point is that countries with centralized institutional solutions are subject to encroaching behaviour by the central government and are threatened with shifting further towards ‘over-centralization’. ‘Over-centralization’ reduces federal member states to pure ‘agents’ of central government. By comparing four federal countries subject to centralization trends (Australia, Austria, Germany and Switzerland) and combinations of causal factors, an attempt is made to ascertain why some federations are locked in ‘over-centralized’ institutional solutions while others are able to ward off such an outcome.

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)0.009
metaresearch head score (Gemma)0.022
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0090.009
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.002

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 head0.156
GPT teacher head0.342
Teacher spread0.185 · 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

Citations35
Published2011
Admission routes1
Has abstractyes

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