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Record W1573630475 · doi:10.1596/1813-9450-5587

Laws for fiscal responsibility for subnational discipline: International experience

2011· book· en· W1573630475 on OpenAlexaboutno aff
Lili Liu, Steven Β. Webb

Bibliographic record

VenueWorld Bank eBooks · 2011
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
FundersBanca d'ItaliaWorld Bank Group
KeywordsPolitical scienceLaw and economicsEconomics

Abstract

fetched live from OpenAlex

Fiscal responsibility laws are institutions with which multiple governments in the same economy-national and subnational-can commit to avoid irresponsible fiscal behavior that could have short-term advantages to one of them but that would be collectively damaging. Coordination failures with subnational governments in the 1980s and 90s contributed to macroeconomic instability and led several countries to adopt fiscal responsibility laws as part of the remedy. The paper analyzes the characteristics and effects of fiscal responsibility laws in seven countries-Argentina, Australia, Brazil, Canada, Colombia, India, and Peru. Fiscal responsibility laws are designed to address the short time horizons of policymakers, free riders among government units, and principal-agent problems between the national and subnational governments. The paper describes how the laws differ in the specificity of quantitative targets, the strength of sanctions, the methods for increasing transparency, and the level of government passing the law.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.004
Scholarly communication0.0030.003
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.064
GPT teacher head0.279
Teacher spread0.216 · 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 designObservational
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

Citations51
Published2011
Admission routes1
Has abstractyes

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