Categorising Electronic Music

Bibliographic Data

ID5048859
AuthorsDanielle Sofer (0000-0002-1863-4904, corresponding author)
Year2020
Volume39
Issue2
Pages231-251
Publication date2020-03-03
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueContemporary Music Review (JOURNAL)
Journal identifiersISSN: 0749-4467 • E-ISSN: 1477-2256
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07494467.2020.1806628
OpenAlexW3083986935
LanguageEN
Citations received2
References cited43

When seemingly automated technologies display biases usually defined as discriminatory human behaviours, these tendencies are referred to collectively as ‘sociotechnical bias’. In this article, I explore facets of racial and gendered sociotechnical bias apparent in the musical categorisation strategies of the database Répertoire International de Littérature Musicale Abstracts of Musical Literature (1967–present only)—or RILM. The article shows how concerns about sociotechnical transparency raised in the digital humanities and computer science are also embedded in musical technologies to advocate for greater awareness and action regarding implicit biases currently operating in musical spheres. The article finds that shared technology among many kinds of electronic music facilitates a kind of musical ‘code-switching’ across genres that, if attuned to appropriately in database administration and keyword designations, would minimise racializing and gendering biases in electronic music categorisation

Art · Electronic music · Knowledge management · Musical · Repertoire · Sociology · Sociotechnical system · Visual arts · Computer Science · Diverse Musicological Studies · Music History and Culture · Music Technology and Sound Studies · Psychology

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