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Record W7137616906

The Forum of Federations Handbook of Fiscal Federalism

2023· other· en· W7137616906 on OpenAlexfundaboutno aff

Bibliographic record

VenueDirectory of Open access Books (OAPEN Foundation) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersDeutsche Akademie der Naturforscher Leopoldina - Nationale Akademie der WissenschaftenEnvironment and Climate Change CanadaUniversidad de ZaragozaUniversitat de BarcelonaCollege of Engineering, Michigan State UniversityUniversiteit StellenboschUniversity of CambridgeUniversidade de FortalezaUnited States Agency for International DevelopmentQueen's UniversityFundação Getulio VargasMinisterio de Ciencia, Innovación y UniversidadesUniversity of TorontoAlbert-Ludwigs-Universität FreiburgFogarty International CenterNorthwestern UniversityAustralian GovernmentPrinceton UniversityEscola Brasileira de Administração Pública e de EmpresasMcGill University
KeywordsFiscal federalismAccountabilityFederalismGovernment (linguistics)GlobalizationDecentralizationEmpowerment
DOInot available

Abstract

fetched live from OpenAlex

This open access handbook compares fiscal federalism arrangements in eleven federal/ decentralized countries. Each chapter examines an individual country, laying out its constitutional design as relates to fiscal powers and the division of those powers between levels of government. Specifically, the analyses consider powers of taxation, spending, regulation, and more. Focusing on Australia, Brazil, Canada, Ethiopia, Germany, India, Italy, South Africa, Spain, Switzerland, and the United States, the contributors provide a fascinating account of how federal countries are confronting the traditional challenges of conflicts over division of fiscal powers while also coping with the ongoing challenges of globalization and citizen empowerment that arise from the information revolution. As a companion to the Forum of Federations Handbook of Federal Countries 2020, this volume considers how relationships and roles in different orders of government are being reshaped, and shows how local solutions inspired by global principles help strengthen government accountability and improve citizens’ quality of life. This is an open access book.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.085
Threshold uncertainty score0.286

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0030.002
Scholarly communication0.0060.006
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0850.028

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 head0.095
GPT teacher head0.421
Teacher spread0.326 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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