Every Track You Take: Analysing the Dynamics of Song and Genre Reception Through Music Downloading
Bibliographic record
Abstract
Using a music-download database provided by the Nokia Corporation, this paper explores three primary questions: (1) Do different musical genres create different patterns of downloading? (2) Do different countries download a particular song in different ways? And (3) how do different events affect downloading? Question (1) is studied in relation to representative songs from the pop, rap and rock genres, and reveals rock to be significantly different from pop and rap in a number of key respects. Question (2) investigates the downloading patterns of a mainstream pop song in Brazil, Germany, Italy, and Mexico. Using Kolmogorov-Smirnoff tests to compare the countries’ download distributions, Brazil is found to be substantially dissimilar to the other countries. And Question (3) is explored with respect to types of event, the first "musical" (Adele's Grammy Awards triumph in February 2012), and the second "extra-musical" (the death of Michael Jackson in June 2009). The extra-musical event is found to have had a far greater effect on downloading patterns than the musical event. The findings are discussed in relation to various factors, including musical genres, the music industry, social media, and globalisation. À l’aide d’une base de données de téléchargement de musique fournie par Nokia Corporation, cet essai explore trois questions principales : (1) Est-ce que différents genres de musique créent différents modèles de téléchargement? (2) Est-ce qu’une chanson en particulier est téléchargée de différentes façons dans différents pays ? Et (3) comment différents événements affectent-ils le téléchargement ? La question (1) est étudiée relativement à des chansons représentatives des genres pop, rap et rock, et révèle que le rock est sensiblement différent des musiques pop et rap à bien des égards. La question (2) examine les modèles de téléchargement d’une chanson pop au Brésil, en Allemagne, en Italie et au Mexique. Faisant appel aux tests de Kolmogorov-Smirnov pour comparer les distributions de téléchargement de ces pays, on a découvert que le Brésil est sensiblement différent des autres pays. Et à la question (3), on explore les types d’événements, premièrement un événement "musical" (le triomphe d’Adele lors des Grammy Awards en février 2012), et deuxièmement un événement "extra-musical" (la mort de Michael Jackson en juin 2009). On a constaté que l’événement extra-musical a eu un effet beaucoup plus important sur les modèles de téléchargement que l’événement musical. Les constatations font l’objet de discussions relativement aux divers facteurs, y compris les genres musicaux, l’industrie de la musique, les médias sociaux et la mondialisation.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".