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NOTAS SOBRE AS POLÍTICAS TERRITORIAIS NO BRASIL E A INSTITUCIONALIZAÇÃO DA REGIÃO METROPOLITANA DO VALE DO PARAÍBA E LITORAL NORTE (RMVPLN)

2017· article· pt· W4312920704 on OpenAlexaff
Marco Antonio Henrique, Adriane Aparecida Moreira de Souza, Paulo Romano Reschilian

Bibliographic record

VenueACTA Geográfica · 2017
Typearticle
Languagept
FieldSocial Sciences
TopicUrban Development and Societal Issues
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O Brasil desde seus primórdios teve como desafio o ordenamento da ocupação e a integração nacional. Com o aumento das atividades econômicas nas cidades, reflexo da dinâmica do capital e que trouxe como consequência o crescimento urbano desordenado, houve a necessidade de se planejar e desenvolver políticas de ordenamento territorial. Por meio de pesquisas bibliográfica e documental referentes à implementação de políticas territoriais, esse estudo discute a criação de regiões metropolitanas no País. Conclui-se que, embora na década de 1970, esse tipo de arranjo institucional sinalizasse para uma centralização política do governo federal, em sua essência propunha a integração e articulação de municípios e a elaboração e implementação de políticas coordenadas que favorecessem o aspecto econômico regional. Com a descentralização política advinda da Constituição Federal de 1988, a criação de tais regiões passou a ser de responsabilidade dos governos estaduais, ao mesmo tempo, que garantiu aos municípios a autonomia na elaboração de políticas de desenvolvimento local. Nesse aspecto, o maior desafio encontrado na governança da Região Metropolitana do Vale do Paraíba e Litoral Norte (RMVPLN), instituída em 2012 é o da implementação de políticas coordenadas que suplantem as diferenças econômicas e promovam o desenvolvimento social dos 39 municípios que a integram.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0040.002
Open science0.0040.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.051
GPT teacher head0.353
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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