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Record W4320731114 · doi:10.1558/prbt.23754

Immersion and metal music videos

2023· article· en· W4320731114 on OpenAlexaff
Elise Girard-Despraulex

Bibliographic record

VenuePerfect Beat · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRepresentation (politics)NarrativeFolkloreAlienDynamismVisual artsRhythmGestureMusicalArtAestheticsHistoryLiteratureSociologyLinguistics

Abstract

fetched live from OpenAlex

Music videos are designed, filmed and edited to magnify the musical experience, and, when well-used, contribute to making artists stand out. With the evolution of media and technology, ‘localness’ can be broadcast worldwide, and folklore, culture and traditions are at the heart of many metal groups’ preoccupations. By making their culture a central part of their music, Alien Weaponry’s success has resulted in the Maori culture, history and legends achieving international recognition in the metal music world. ‘Kai Tangata’ and ‘Hatupatu’, the music videos directed by Alex Hargreaves, operate to further represent elements of Maori culture, by adding a visual dimension to Alien Weaponry’s use of te reo Maori, the Maori language. Using formal and comparative aesthetical analyses, reinforced by a theoretical approach, the use of immersion in this representation will be discussed. Firstly, the representation of the characters in the videos and their role in the narration will be analysed. Secondly, the affect and the dynamism brought by the rhythm and the structure of music and images will be examined. And finally, the representation of bodies, gestures and rituality will be analysed, as a representation of the Maori culture, meant for both Maori and non-Maori people.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.191
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.041
GPT teacher head0.305
Teacher spread0.264 · 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 teacher head, 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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