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Record W2505547929 · doi:10.1017/cbo9781139175166.017

Ensuring a Stable Federal State: Economics or Political Institutional Design

2001· book-chapter· en· W2505547929 on OpenAlexaboutno aff
Михаил Филиппов, Peter C. Ordeshook, Olga Shvetsova

Bibliographic record

VenueCambridge University Press eBooks · 2001
Typebook-chapter
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsState (computer science)PoliticsPolitical scienceInstitutional economicsPublic administrationEconomicsLaw and economicsPolitical economyNeoclassical economicsComputer scienceLaw

Abstract

fetched live from OpenAlex

Federalism is often seen as a partial solution to an array of problems that confound stability and economic growth in democratic states. For continental powers like the United States, Russia, and Australia, the goal is to encourage a rational treatment of public goods and externalities that vary in geographic scope. Federalism in Germany was identified as a way to decentralize power in accord with earlier traditions and to minimize the likelihood that a dictatorship could again subvert its constitutional order. Federalism in India seems the only way to govern a heterogeneous state that will soon be the most populous on the planet. Federal institutions in Russia are essential not only because of geographic diversity and the desire to break with the previous regime's practices of over-centralization, but also to make coherent a situation in which regional authorities cannot be precluded from asserting their autonomy against a weakened central government. And federalism in one form or another is regarded as the only structure that might contend successfully with the ethnic, religious, and linguistic cleavages that bedevil countries like Spain, Ukraine, Nigeria, Belgium, South Africa, and Canada. In fact, it is now commonly agreed that there should be some federal-like decentralization of governmental authority and responsibility even for states that are not explicitly or implicitly federal.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.060
GPT teacher head0.241
Teacher spread0.181 · 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

Citations13
Published2001
Admission routes1
Has abstractyes

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