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Record W7131284961 · doi:10.12957/riae.2025.82456

POR UMA EDUCAÇÃO ANTIRRACISTA EM UM CONTEXTO NEOLIBERAL

2025· article· W7131284961 on OpenAlexaboutno aff
Brenda Cristina Da Silva e Silva, Fabrícia Vellasquez Paiva

Bibliographic record

VenueRevista Interinstitucional Artes de Educar · 2025
Typearticle
Language
FieldSocial Sciences
TopicRace, Identity, and Education in Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeContext (archaeology)SituatedOrder (exchange)Relation (database)

Abstract

fetched live from OpenAlex

O presente artigo busca debater sobre os limites e possibilidades da Lei 10.639/03 nas escolas brasileiras, diante de um cenário de desmontes e de desqualificação das instituições públicas de ensino a partir da lógica neoliberal, e como as obras de narrativas literárias de autores e autoras negras podem auxiliar no debate e na reflexão da questão étnico-racial em sala de aula e em toda a comunidade escolar. Para tal, foi feita uma revisão bibliográfica que nos permitiu analisar como o neoliberalismo se estrutura na sociedade e influencia as relações sociais, bem como textos que se debruçam sobre a Lei 10.639/03 e sobre o uso de literaturas para a sua aplicabilidade. Como referenciais teóricos bases, usamos Antônio Cândido (2011), Edward Said (2011) e Spivak (2010), para argumentar sobre a potencialidade da Literatura. O texto de Pierre Dardot e Christian Laval (2016), foi utilizado como base para pensarmos o neoliberalismo e a sua influência na educação. A partir da análise bibliográfica, portanto, foi possível perceber como a literatura pode servir como um valioso instrumento crítico-reflexivo, buscando uma educação antirracista. Palavras-chave: Lei 10.639/03; Literatura; Educação; Antirracista; Neoliberalismo.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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 head0.034
GPT teacher head0.378
Teacher spread0.344 · 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
Published2025
Admission routes1
Has abstractyes

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