Review: Spotify Teardown: Inside the Black Box of Streaming Music, by Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars, and Patrick Vonderau
Bibliographic record
Abstract
Book Review| August 27 2020 Review: Spotify Teardown: Inside the Black Box of Streaming Music, by Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars, and Patrick Vonderau Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars, and Patrick Vonderau. Spotify Teardown: Inside the Black Box of Streaming Music. Cambridge, MA: MIT Press, 2019. 288 pages. James Deaville James Deaville Carleton University Email: JamesDeaville@cunet.carleton.ca James Deaville teaches music in the School for Studies in Art and Culture at Carleton University, Ottawa. He edited Music in Television: Channels of Listening (Routledge, 2010) and with Christina Baade co-edited Music and the Broadcast Experience:Performance, Production, and Audiences (Oxford, 2016). He has published articles on music and sound in film trailers in Music, Sound and the Moving Image (2014) and in the Journal of Fandom Studies (2016), and is author of the essay “Trailer or Leader? The Role of Music and Sound in Cinematic Previews” in the Routledge Companion to Screen Music and Sound (2017). He is currently publishing the article “The Trailer Ear” in The Oxford Handbook of Cinematic Listening, edited by Carlo Cenciarelli. He is co-editing with Ron Rodman and Siu-Lan Tan the Oxford Handbook of Music and Advertising, to which he has contributed a chapter on television promos. Search for other works by this author on: This Site PubMed Google Scholar Journal of Popular Music Studies (2020) 32 (3): 153–155. https://doi.org/10.1525/jpms.2020.32.3.153 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation James Deaville; Review: Spotify Teardown: Inside the Black Box of Streaming Music, by Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars, and Patrick Vonderau. Journal of Popular Music Studies 27 August 2020; 32 (3): 153–155. doi: https://doi.org/10.1525/jpms.2020.32.3.153 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentJournal of Popular Music Studies Search We begin this review by naming what the musician might consider the book’s most striking feature, which is an absence, in this case the absence of music: throughout the text, no composer nor songwriter is named, no album nor track identified by title other than the fake album Election Music by fictitious creator Hein Duthel. This lack must strike the reader/listener still anchored in the visceral enjoyment of sound as highly unusual and ironic, considering that the authors’ subject is the streaming service that is supposed to fulfill our every musical desire or need. Or we could interpret the book’s text itself as a shrewd enactment of Spotify’s own covert strategy of shifting from music as product to the music consumer as product: Spotify Teardown simply cleverly performs the service’s own de- and re-commodification. Read on its surface, Spotify Teardown draws our attention to surprising facts about the streaming platform,... You do not currently have access to this content.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".