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Record W2058304725 · doi:10.1515/zfse.2.2.191

Bundesstaaten unter Reformdruck: Kann Deutschland von Kanada lernen?

2004· article· de· W2058304725 on OpenAlexaboutno aff
Rainer-Olaf Schultze

Bibliographic record

VenueZeitschrift für Staats- und Europawissenschaften · 2004
Typearticle
Languagede
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Infolge von Globalisierung, Europäisierung und Wiedervereinigung haben sich die normativen und gesellschaftlichen Grundlagen des deutschen Bundesstaats nachhaltig verändert; ohnehin vorhandene Funktionsprobleme des verflochtenen Institutionensystems wurden dadurch verschärft. Mit Blick auf die unausweichliche Strukturreform des bundesdeutschen Föderalismus führt der Vergleich mit Kanada zu interessanten Befunden. Dies gilt sowohl für Reformoptionen, wie eine „indirekte Entflechtung“ von Gesetzgebungskompetenzen oder eine Lockerung des Steuerverbunds, als auch für die allgemeinere Einsicht, dass sich föderalstaatliche Kooperation und Integration auch bei weitreichender Dezentralisierung und unter Wettbewerbsbedingungen herstellen lassen. Abstract The processes of globalisation, Europeanisation and re-unification have significantly changed the normative and social dimensions of German federalism, aggravating the already existing functional problems caused by a heavily interlocked institutional system. A structural reform of the federal state seems, therefore, inevitable. Comparing the German with the Canadian experience highlights a number of interesting reform options, especially concerning the “indirect disentanglement” of competencies and the flexibilisation of the tax union between Bund and Länder. The analysis concludes that federal co-operation and integration are also to be achieved within highly decentralised and competitive contexts.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.602
Threshold uncertainty score0.790

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0060.006
Scholarly communication0.0140.008
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.017
GPT teacher head0.300
Teacher spread0.282 · 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
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

Citations3
Published2004
Admission routes1
Has abstractyes

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