Review: I'm Not Like Everybody Else: Biopolitics, Neoliberalism, and American Popular Music by Jeffrey T. Nealon
Bibliographic record
Abstract
Book Review| March 01 2020 Review: I'm Not Like Everybody Else: Biopolitics, Neoliberalism, and American Popular Music by Jeffrey T. Nealon Jeffrey T. Nealon, I'm Not Like Everybody Else: Biopolitics, Neoliberalism, and American Popular Music. Lincoln and London: University of Nebraska Press, 2018. 130 pp. Natalie Farrell Natalie Farrell University of Chicago Email: farrelln@uchicago.edu Natalie Farrell is a Ph.D. student in music history/theory at the University of Chicago. Her work has been published in Music and Letters and The Flutist Quarterly, and she has presented in conferences across the Midwest and Toronto. In 2017, she received a grant from the Eastman School of Music's Paul R. Judy Center for Innovation and Research to explore “hip consumerism” and the Indianapolis Symphony/New Amsterdam partnership. Her interests include cruel optimism and the art music industry post-1990, sound studies, trauma theory, and traditional dance music in Northern Ireland. In her free time, she likes to knit and spend time with her dog (who is named after Leonard Bernstein). Search for other works by this author on: This Site PubMed Google Scholar Journal of Popular Music Studies (2020) 32 (1): 130–132. https://doi.org/10.1525/jpms.2020.32.1.130 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Natalie Farrell; Review: I'm Not Like Everybody Else: Biopolitics, Neoliberalism, and American Popular Music by Jeffrey T. Nealon. Journal of Popular Music Studies 1 March 2020; 32 (1): 130–132. doi: https://doi.org/10.1525/jpms.2020.32.1.130 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of Popular Music Studies Search A few years ago, a friend introduced me to William Onyeabor's infectiously feel-good early synth-funk song, “Fantastic Man.” I was equally intoxicated by the electric organ solos that seemed to go on a little too long and Onyeabor's elusive backstory: he emigrated from Nigeria to New York in the late 1970s, self-produced a series of recordings that influenced the burgeoning underground hip-hop scene, and later rejected music altogether to become a pastor in Nigeria. I searched used record store bins for one of his long-lost self-made pressings, grooved along to a low-definition MP3, and felt cool. A real, authentic cool—not like those hipsters a few chairs down from me in the... © 2020 by the Regents of the University of California. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press's Reprints and Permissions web page, https://www.ucpress.edu/journals/reprints-permissions.2020 You do not currently have access to this content.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".