Humans thrive on placing everyday perceptions into discrete categories. These categories, including genres in popular music, help us understand the world around us and process new information in a relatively efficient way. But music theorists often take genre for granted, by which I mean most scholars rarely unpack how social and musical categories work together to codify and reinforce often racial and gendered lines placed between categories of people and music. In Reconceiving Genre, I address some the methodological issues around genre in popular music.
This dissertation argues for a reframing of genre and what these labels can convey. Through different generic case studies in each chapter, I develop a framework for analyzing genre from multiple musical and cultural traditions. I critically examine social and commercial constructions of popularity and authenticity in relation to artist’s presentations of gender, race, class, and sexuality— positionalities that are often taken for granted in popular music analysis.
This dissertation’s most distinctive contribution to both music theory and popular music studies is that it dismantles the idea that genre is only a marketing label that conveys details about an artist’s musical style, and challenges assumptions that genre is “dead” or beyond repair to be a meaningful tool. These assumptions dull the transgressive force of how we can use genre to understand issues of authenticity, popularity, and society more broadly. In short, my project demonstrates that genre categories can reveal as much about an artist’s identity as they can about a piece’s musical features. Thus, theories of genre need to account for identities like race, gender, and sexuality of artists, listeners, and even analysts in order to be fully inclusive, meaningful, and accessible.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/t5tb-w493 |
| Date | January 2024 |
| Creators | Shepherd, Lauren Marilyn |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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