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Record W4390134933 · doi:10.30905/rde.v7i1.816

Docilidade e disciplinamento do professor no contexto do projeto educativo neoliberal

2023· article· pt· W4390134933 on OpenAlexaboutno aff
Carlos Betlinski, Wesley Dias Dos Santos

Bibliographic record

VenueDEVIR EDUCAÇÃO · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSociologyRationalityPhilosophyEpistemology

Abstract

fetched live from OpenAlex

O presente artigo tem como objetivo identificar os dispositivos da racionalidade neoliberal, na produção do disciplinamento docente, que se dá, na maioria das vezes, mediado por um discurso de progresso e inovação. O arquétipo do trabalhador, como empresário de si mesmo, acaba por responsabilizar os sujeitos pelo sucesso ou fracasso das atividades desenvolvidas, sempre numa perspectiva individualista que altera a compreensão da responsabilização coletiva e do próprio Estado pela execução dos projetos educativos. Para entendermos o trabalho docente, que é nosso objeto de investigação, buscamos referências teóricas em Michel Foucault (2007), Pierre Dardot & Christian Laval (2016) e Ricardo Antunes (2020) e, numa abordagem contextualizada junto às alterações do sistema produtivo capitalista, vinculamos a racionalidade neoliberal com o projeto de educação mercadológico para, então, compreendermos a configuração do trabalho docente, caracterizado pelos princípios do empreendedorismo, da meritocracia, concorrência e com perspectiva de formação pragmática vinculada à racionalidade instrumental. As análises permitiram concluir que há forte índice de interferência dos discursos e dos princípios empresariais, na organização da educação pública e, em consequência, na configuração do trabalho docente.

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)0.007
metaresearch head score (Gemma)0.016
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.007
Scholarly communication0.0120.004
Open science0.0020.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.003

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 head0.172
GPT teacher head0.502
Teacher spread0.331 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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