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ABORDAGEM METODOLÓGICA DE M. SCHAFER PARA O ENSINO DE MÚSICA NA ESCOLA

2014· article· pt· W3046850267 on OpenAlexaboutno aff
Patrícia L. L. Mertzig Gonçalves de Oliveira, Marcelo Saldanha das Neves, Roberta Negrão de Freitas

Bibliographic record

VenueColloquium humanarum · 2014
Typearticle
Languagept
FieldSocial Sciences
TopicMusic Education and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

O presente artigo de caráter teórico tem como objetivo apresentar a abordagem metodológica do compositor canadense M. Schafer para o ensino de música no espaço escolar.Schafer coloca a atenção perceptiva na escuta e para fomentar sua abordagem utiliza do conceito de Paisagem Sonora.Grande parte das metodologias utilizadas para a educação musical concentra a audição em repertórios de um sistema musical específico, pertencente a um período histórico específico.Schafer, enquanto compositor contemporâneo propõe que a escuta se inicie pela Paisagem Sonora, ou seja, atenção a todos os sons que compõem os ambientes em que estamos inseridos.A abordagem de Schafer será então vista sob a ótica da fenomenologia e ecologia.Acredita -se que o uso da Paisagem Sonora pode auxiliar no processo de percepção auditiva e na manutenção da escuta como um hábito, características imprescindíveis à educação musical no espaço escolar.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.079
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0060.010
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0020.003
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 head0.054
GPT teacher head0.358
Teacher spread0.303 · 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".

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Citations1
Published2014
Admission routes1
Has abstractyes

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