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Record W4290549295 · doi:10.1386/mms_00075_1

Threat cues in metal’s visual code

2022· article· en· W4290549295 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 · 2022
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsBrandon University
Fundersnot available
KeywordsCode (set theory)LuminancePsychologyCommunicationCognitive psychologyComputer scienceArtificial intelligenceSet (abstract data type)

Abstract

fetched live from OpenAlex

Metal music has a signifying visual aesthetic or so-called visual code. In this article, I examine the significance of four primitive visual properties that characterize the code. I propose that the propensity for visual artefacts of metal music to depict low luminance, low colourfulness, redness and angular shapes is because these properties can act as subtle threat cues. As metal music has a preoccupation with threatening themes and sounds, these four properties provide concordant visual information about the affective attributes of the genre. After reviewing the supporting psychological evidence, I conduct a quantitative test of this proposal by comparing album covers from extreme metal bands to those from children’s music, two genres that are opposed to one another in their embrace of a threatening atmosphere. The results indicate that extreme metal album covers are darker, less colourful, redder and feature more angular shapes compared with their children’s counterparts, which suggests that these properties are relied upon to communicate a clear threat signal.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.987

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.001

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 head0.187
GPT teacher head0.420
Teacher spread0.233 · 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