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Record W1972548698 · doi:10.1177/1091142103031004003

The Measurement of Interregional Redistribution

2003· article· en· W1972548698 on OpenAlexaff
Giuseppe C. Ruggeri, Weiqiu Yu

Bibliographic record

VenuePublic Finance Review · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRedistribution (election)Per capitaEconomicsRevenueFiscal federalismPer capita incomeFederal stateRedistribution of income and wealthDemographic economicsMacroeconomicsPopulationDecentralizationEconomic policyDemographyPolitical scienceUnemployment

Abstract

fetched live from OpenAlex

This study develops local and global indices to measure interregional redistribution. The local indices compare federal revenues and expenditures assigned to various regions with the pattern of income disparities among regions. They are developed by relating these comparisons under a given regional distribution of federal fiscal activity to three special cases under known degrees of redistribution: no redistribution, the degree of redistribution that would be delivered solely through equal per capita federal spending in each region, and the maximum degree of redistribution that would equalize per capita income in all regions. Global indices are then developed as weighted averages of the local indices. Results show that the federal fiscal system in 1996 delivered a degree of interregional redistribution 1.8 times what would have been generated under equal per capita federal expenditures in each region and nearly half of the maximum redistribution that could have been delivered by the federal fisc.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.093
GPT teacher head0.241
Teacher spread0.149 · 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

Citations5
Published2003
Admission routes1
Has abstractyes

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