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Record W7120641710

From transgression to conservatism: the escalation of the extreme right in the metal scene.

2019· dissertation· pt· W7120641710 on OpenAlexaboutno aff
Raffael Silveira. SENA

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019
Typedissertation
Languagept
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Musical instrumentMusicalNova scotia
DOInot available

Abstract

fetched live from OpenAlex

O Heavy Metal é o resultado de uma teia de acontecimentos relacionados ao campo musical ocorridos em meados dos anos de 1960 e início dos anos 1970 e ganhou definições e força definitiva nos anos 1980 com a Nova Onda do Heavy Metal britânico, mesma época em que começou a sua fragmentação em diferentes subgêneros como o Trash Metal, Death Metal e Black Metal, que terminaram por compor a cena Metal. No que tange a gênese do Heavy Metal e sua implicação social, o subgênero ficou marcado não só pela música “pesada” e pela estética “chocante”, mas também por atacar valores conservadores moralmente aceitos pela sociedade daquela época. Porém no atual contexto sócio-histórico, onde as contradições sociais e ideológicas no Brasil e no mundo se encontram ainda mais acentuadas, vemos emergindo no movimento a reprodução de discursos e ideologias conservadoras e de extrema direita. A incorporação dessas idéias deu origem a um subgênero chamado de Black Metal Nacional Socialista ou NSBM. O presente trabalho possui como objetivo analisar a escalada do conservadorismo e da extrema direita na cena Metal, além de resgatar e apreender suas origens e dinâmicas sociais, considerando seus símbolos, códigos e significados, bem como construções mentais imbricadas nos discursos e performances dos atores pertencentes à cena Metal.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.045
Scholarly communication0.0090.007
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.035
GPT teacher head0.241
Teacher spread0.207 · 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
Published2019
Admission routes1
Has abstractyes

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