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Record W3028078783 · doi:10.5216/ia.v45i1.61630

O RETROCESSO DA REFORMA DO ENSINO MÉDIO, A BNCC, O NEOLIBERALISMO EDUCACIONAL E A MARGINALIZAÇÃO DOS INSTITUTOS FEDERAIS - IFs

2020· article· pt· W3028078783 on OpenAlexaboutno aff
Rosimar Serena Siqueira Esquinsani, Sidinei Cruz Sobrinho

Bibliographic record

VenueRevista Inter-Ação · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhysicsPhilosophy

Abstract

fetched live from OpenAlex

RESUMO O artigo analisa e discute as principais justificativas governamentais para a política pública educacional da Reforma do Ensino Médio (2017), e a Base Nacional Comum Curricular para o Ensino Médio (BNCC-EM, 2018). Demonstra o conflito da compreensão de Educação para a Formação Integral, explícita no Art. 205 da Constituição Federal de 1988, com a ideia em desenvolvimento para uma educação mercantilista neoliberal. Afirma o retrocesso do “Novo Ensino Médio” comparado as reformas da mesma etapa de ensino durante a ditadura militar e, em nível mundial, a partir de referenciais como as reformas na França e Estados Unidos. A pesquisa se fundamenta a partir da hermenêutica dos argumentos da exposição de motivos para a Lei 13.415/2017, declarações do governo (MEC) na mídia, estudos e relatórios sobre o tema e fundamentação de aporte teórico em Dardot, Laval e outros. Conclui pela refutação à lógica neoliberal da reforma do ensino médio e no currículo por habilidades e competências na BNCC. Sugere a retomada e a ampliação da experiência dos Institutos Federais – IFs na perspectiva da formação integral e da educação profissional integrada ao ensino médio na educação pública. Palavras-chave: Reforma do Ensino Médio, BNCC, Neoliberalismo, Institutos Federais – Ifs.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.011
Scholarly communication0.0080.004
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.127
GPT teacher head0.392
Teacher spread0.265 · 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 designQualitative
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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