From Bill Clinton playing his saxophone on The Arsenio Hall Show to Barack Obama referencing Jay-Z's song "Dirt Off Your Shoulder," politicians have used music not only to construct their personal presidential identities but to create the broader identity of the American presidency. Through music, candidates can appear relatable, show cultural competency, communicate values and ideas, or connect with a specific constituency. On a less explicit level, episodes such as Clinton's sax-playing and Obama's shoulder brush operate as aural and visual articulations of race and racial identity. But why do candidates choose to engage with race in this manner? And why do supporters and detractors on YouTube and the Twittersphere similarly engage with race when they create music videos or remixes in homage to their favorite candidates?
With Barack Obama, Ben Carson, Kamala Harris, and Donald Trump as case studies, Tracks on the Trail: Popular Music, Race, and the US Presidency (U Michigan Press, 2023) sheds light on the factors that motivate candidates and constituents alike to articulate race through music on the campaign trail and shows how the racialization of sound intersects with other markers of difference and ultimately shapes the public discourse surrounding candidates, popular music, and the meanings attached to race in the 21st century. Gorzelany-Mostak explores musical engagement broadly, including official music in the form of candidate playlists and launch event setlists, as well as unofficial music in the form of newly composed campaign songs, mashups, parodies, and remixes.
This interview was conducted by Dr. Miranda Melcher whose new book focuses on post-conflict military integration, understanding treaty negotiation and implementation in civil war contexts, with qualitative analysis of the Angolan and Mozambican civil wars.
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Dana Gorzelany-Mostak, "Tracks on the Trail: Popular Music, Race, and the US Presidency" (U Michigan Press, 2023) | New Books in Political Science podcast - Listen or read transcript on Metacast
