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Record W7017123301

Análise do desempenho do PIB dos estados nordestinos e sua relação com as transferências federais e receitas próprias

2013· article· en· W7017123301 on OpenAlexaboutno aff

Bibliographic record

VenueAmericanae (AECID Library) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueEconometric modelVariable (mathematics)VariablesProduct (mathematics)Tax revenueQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

The economic dependence of the northeastern states of the resources of the central entity in the Federal Republic of Brazil still dominates the economic situation of the public sector today. However, with more aggressive policies in respect to the economy, especially during 1990 and 2000 shows that gradually, own resources, derived from the collection of taxes, provide a less dependence on constitutional transfers to these states. This situation can be proven observers described the increase own revenues of the past years. Based on this assumption that the objective is to study the present relationship of the independent constitutional transfers and own revenues with the dependent variable growth rate of Gross Domestic Product (GDP) for the nine states of the federation located in northeastern Brazil from 1988 to 2008. The methodology for this test is used as a mechanism econometric model, and a literature review. From the roll of the dice was taken as a result of a relationship with a positive sign and statistical significance of the variable "own revenue" with the phenomenon studied. Another important conclusion is that the predictor variable "constitutional transfers", despite a positive sign, did not represent statistical significance.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.251
Teacher spread0.241 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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