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Record W2506812016 · doi:10.22456/1982-1654.58362

Cena e Computadores: fricções para uma pedagogia do teatro expandido

2016· article· pt· W2506812016 on OpenAlexaff
Fernanda Areias Oliveira, Juan Carlos Castro

Bibliographic record

VenueInformática na educação teoria & prática · 2016
Typearticle
Languagept
FieldArts and Humanities
TopicArts and Performance Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

A nova perspectiva na arte contemporânea apresenta objetos artísticos que se utilizam do computador ou de máquinas analógicas para propor diferentes formas de apreciação. No teatro, observamos uma forte tendência no uso da tecnologia do vídeo em cenários, projeções em corpos e performances interativas. Com uma perspectiva diferente, na pedagogia do teatro ainda trabalhamos com um forte vinculo à cena clássica, baseando sua estruturação em jogos teatrais. Este artigo pretende discutir o atual estado do teatro e tecnologia nas aulas de teatro do Brasil. Nossa questão central é baseada a articulação entre a tendência em arte contemporânea e as reais possibilidades em nossas aulas de teatro. Para nosso suporte teórico, nos articularemos as ideias de Philip Auslander , e sua atenção sobre o novo espectador imerso no contexto da mídia digital, Michael Anderson e as atuais circunstâncias das tecnologias nas aulas de teatro, em diálogo com o pesquisadores da arte educação brasileiros. Palavras chave: Pedagogia do Teatro, Arte Educação e Formação de Professores.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.011
Scholarly communication0.0100.009
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0190.002

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 head0.046
GPT teacher head0.296
Teacher spread0.250 · 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 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
Published2016
Admission routes1
Has abstractyes

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