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Record W3113951597 · doi:10.5380/dma.v55i0.73031

Representações dos retirantes das secas do Semiárido nordestino

2020· article· pt· W3113951597 on OpenAlexaff
José Gomes Ferreira, Anna Lidiane Oliveira Paiva, Anastácia Brandão de Mélo

Bibliographic record

VenueDesenvolvimento e Meio Ambiente · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

As mudanças climáticas e as projeções no sentido do seu agravamento no Nordeste aumentam as preocupações quanto às repercussões no meio ambiente, na economia e na vida das comunidades. Entre os impactos sociais mais estudados destacam-se as migrações, temporárias ou permanentes, que integram os chamados refugiados climáticos. O semiárido nordestino é uma vasta região do território brasileiro historicamente exposta às condições severas do clima seco, em que longos períodos de estiagem obrigavam o sertanejo a refugiar-se em grandes cidades do litoral em busca de sobrevivência. O chamado de retirante, ou flagelado da seca, é o elemento mais fragilizado na hierarquia social e aquele que mais depressa é afetado pela ausência prolongada de chuvas na região. O artigo analisa as representações sociais dos emigrantes sertanejos por meio de bibliografia científica, mídia e literatura, buscando melhor conhecer esse fenômeno e contribuir para a resiliência das comunidades em um contexto de agravamento dos fenômenos climáticos extremos. Os resultados encontrados foram as diversas representações dos emigrantes nordestinos, o preconceito, os impactos das secas, o drama social e político, as relações de dominação, a memória de um povo e a necessidade de políticas públicas estruturantes.

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)0.001
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 score0.205
Threshold uncertainty score0.408

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.176
GPT teacher head0.359
Teacher spread0.182 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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