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Record W7131313341 · doi:10.7202/1122630ar

Parenting with Metal: Extreme Media Literacy Between Generations

2025· article· fr· W7131313341 on OpenAlexvenueaboutno aff
Ruth Barratt-Peacock

Bibliographic record

VenueIntermédialités Histoire et théorie des arts des lettres et des techniques · 2025
Typearticle
Languagefr
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive dissonanceMedia literacyLiteracyReading (process)Raising (metalworking)Value (mathematics)

Abstract

fetched live from OpenAlex

Reading interviews published in Canadian magazine Hellbound, I trace a bidirectional dissonance between metal parents’ metal media practices and their role as parents. These “metal parents” address friction between these roles by asserting metal music’s value as a tool for helping children develop mental health management strategies and the media literacy to critically engage with sexual content in other popular media. The latter becomes particularly important in connection to children growing up with metal containing sexual and violent content. Whether we frame it as growing up with metal or raising children with metal, these interviews indicate that there is a significant shift towards intergenerational media practices in consumers of metal music, to which the industry is slowly adapting. Nevertheless, parents appear to still approach metal in the home with ever-shifting compromises and, above all, active engagement between child, parent, and text.

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)0.004
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.411

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0150.011
Scholarly communication0.0070.005
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.055
GPT teacher head0.293
Teacher spread0.239 · 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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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