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Record W4387408689 · doi:10.46814/lajdv5n2-015

O dilema do desenvolvimento econômico em meio à escassez de recursos e à dependência de exportações de commodities

2023· article· pt· W4387408689 on OpenAlexaff
Valentin Aguiar Filho, Josélia Barros Lima Aguiar

Bibliographic record

VenueLatin American Journal of Development · 2023
Typearticle
Languagept
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhysicsAgricultural sciencePhilosophyBiology

Abstract

fetched live from OpenAlex

Na escalada de desenvolvimento econômico de qualquer país, o emprego sustentável de recursos contribui significativamente para o aumento da produção e da renda per capita da população, além de viabilizar a geração de divisas, oriundas de receitas de exportações. Todavia, grande parte das nações em desenvolvimento não dispõem de volume e qualidade de recursos de produção suficientes para alavancar o crescimento de seu produto interno bruto, comprometendo a implementação de etapas de desenvolvimento econômico, inviabilizando a melhoria de qualidade de vida de sua população. Em outras nações, o valor econômico geralmente atribuído ao produto gerado, como, no caso das commodities, é muitas vezes subestimado, levando-as a um caminho insustentável de crescimento econômico. Assim, busca-se evidenciar, por meio deste estudo, que a escassez de recursos de produção, aliado à dependência de exportações de commodities gera movimentos de instabilidade nas reservas cambiais e incertezas macroeconômicas. Nesta perspectiva, pode-se inferir que a agregação de valor aos produtos gerados requer conhecimento, acesso à tecnologia, base infraestrutural satisfatória, adequado volume e qualidade de insumos, os quais uma economia dependente de commodities pode não dispor.

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)0.005
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.003
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.028
GPT teacher head0.242
Teacher spread0.214 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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