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Record W4388631938 · doi:10.1590/1414-462x202331030261

Impactos da pandemia do novo coronavírus no direito humano à alimentação e à nutrição adequadas e programas de segurança alimentar e nutricional: quais os desafios e o que propor?

2023· article· pt· W4388631938 on OpenAlexaff
Maylla Luanna Barbosa Martins Bragança, Alessandra Karla Oliveira Amorim Muniz, Lívia Carolina Sobrinho Rudakoff, Marjorie Lima do Vale

Bibliographic record

VenueCadernos Saúde Coletiva · 2023
Typearticle
Languagept
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGovernoPolitical scienceAgricultural scienceHumanitiesBiologyPhilosophy

Abstract

fetched live from OpenAlex

Resumo Introdução A pandemia do novo coronavírus intensificou a violação do direito humano à alimentação e nutrição adequadas e elevou a insegurança alimentar e nutricional na população brasileira. Objetivo Analisar as estratégias das políticas e programas de segurança alimentar e nutricional adotadas pelo governo brasileiro durante a pandemia (ano de 2020) para promoção da segurança alimentar e nutricional e combate à insegurança alimentar e nutricional no país. Método Realizou-se busca nos sites oficiais para análise das ações propostas. Resultados Apesar de algumas proposições pontuais, como a distribuição do auxílio emergencial, estratégias mais enérgicas, como o fortalecimento do Programa de Aquisição de Alimentos, encorajamento de pequenos agricultores, facilitação do transporte e da comercialização de alimentos e a garantia da continuidade de programas assistenciais que facilitem acesso à renda e à alimentação durante a pandemia, precisam ser urgentemente adotadas. Conclusão Essas estratégias requerem atuação das três esferas de governo em prol do combate dos impactos da pandemia do novo coronavírus nos índices de insegurança alimentar e nutricional 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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient 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 score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.012

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 head0.190
GPT teacher head0.458
Teacher spread0.268 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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