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Record W2333248030 · doi:10.1386/mms.2.1.87_1

Finally getting out of the maze: Understanding the narrative structure of extreme metal through a study of ‘Mad Architect’ by Septicflesh

2016· article· en· W2333248030 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMetal Music Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLyricsNarrativeTimbreNarratologyHarmony (color)MelodyLiteratureAlienationMusicalLamentArtAestheticsPsychologyVisual arts

Abstract

fetched live from OpenAlex

Many academics assume that extreme metal music cannot be identified to an understandable narrative, because of the difficulty to perceive the lyrics and the poor harmonic and melodic structure. The author of this paper demonstrates the opposite, using a methodology called narratology, derived from literary studies and musicology. She analyses the song ‘Mad Architect’ from symphonic death metal group Septicflesh (2011), which narrates the alienation felt by a protagonist who thinks that he is lost in a maze before he realizes that this maze is actually a construction of his mind. The specific narrative of the song, which evokes the madness and the alienation felt by the protagonist, will be better understood by analysing jointly the lyrics (using narratological parameters being the time, the modality and the voice of the narrative) and the musical parameters (namely harmony, melody, vocal timbre or sound technology).

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.

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 score0.033
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.181
GPT teacher head0.275
Teacher spread0.094 · 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