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Orthogonality, dependency, and music: An exploration of the relationships between music facets
Author(s) -
Lee Deborah,
Robinson Lyn,
Bawden David
Publication year - 2021
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24426
Subject(s) - orthogonality , dependency (uml) , construct (python library) , facet (psychology) , computer science , boundary (topology) , epistemology , cognitive science , psychology , artificial intelligence , mathematics , social psychology , philosophy , geometry , personality , big five personality traits , programming language , mathematical analysis
The classification of Western art music is a complex area of knowledge organization, yet the reasons for those complexities have not been fully studied. This research dissects the concept of orthogonality, in particular regard to music classification. Orthogonality (antonym: dependency) means that one facet acts independently from another facet. Although orthogonality is an assumed quality of facets, it has attracted relatively little attention in knowledge organization discourse. This research utilizes bibliographic classification schemes, musicological writings, and musical works to analyze orthogonality in music classification. The relationships between the medium, form/genre, and function facets are unpicked and a strong dependency is found between these facets. Whether this orthogonality exists as a construct of faceted classification or stems from the domain knowledge is explored. Furthermore, the analysis initiates new thinking about the general concept of orthogonality. The idea of a spectrum of dependency is proposed. In addition, novel, orthogonality‐derived phenomena are discussed—“dynamic facets” and “meta‐dependency”—where the boundary between what is and is not a facet is blurred. The concluding model visualizes the chain of dependencies between music facets, ultimately showing how the lack of orthogonality plays a key role in the complexity and issues found in music classification.