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Record W3165033153 · doi:10.4000/rccs.11605

A visão utópica da CEPAL e a desigualdade de renda no Brasil

2021· article· pt· W3165033153 on OpenAlexaff
Fernando Augusto Mansor de Mattos, João Hallak Neto, Marcelo Weishaupt Proni

Bibliographic record

VenueRevista crítica de ciências sociais/Revista crítica de ciências sociais · 2021
Typearticle
Languagept
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsInequalityEconomic inequalityIncome distributionEconomicsSociologyDevelopment economicsMathematics

Abstract

fetched live from OpenAlex

Em 2014, a Comissão Econômica para a América Latina e o Caribe (CEPAL) afirmava que uma estratégia de desenvolvimento direcionada para maior equidade econômica, maior justiça social e maior proteção ambiental poderia ser adotada em vários países da América Latina – um cenário que, no entanto, se desfigurou nos últimos anos. Este artigo tem assim três objetivos: (1) analisar o caráter utópico da agenda da CEPAL para a promoção de sociedades mais igualitárias na América Latina; (2) elencar os principais determinantes da redução da desigualdade de renda no Brasil no período 2004-2014; e (3) examinar a interrupção da trajetória de redução da desigualdade de renda – a partir do golpe político de 2016 e da aprovação de reformas neoliberais que aumentaram a precariedade dos empregos e enfraqueceram o sistema de proteção social –, como evidência da interdição de uma estratégia de desenvolvimento inclusivo no Brasil.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0040.006
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.062
GPT teacher head0.341
Teacher spread0.279 · 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
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

Citations1
Published2021
Admission routes1
Has abstractyes

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