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Record W2265327770 · doi:10.16995/dscn.25

Generalizing case-based analyses in the study of global music consumption

2016· article· en· W2265327770 on OpenAlex
Matthew Woolhouse, James Renwick

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueDigital Studies / Le champ numérique · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDownloadUploadMusicalHumanitiesConsumption (sociology)ArtComputer scienceHistoryWorld Wide WebLiteratureAesthetics

Abstract

fetched live from OpenAlex

Using large-scale analysis of a music-download database provided by MixRadio, a leading online music-service provider formerly under Nokia ownership, this paper investigates the following in relation to global-music consumption: (1) differences in the downloading trajectories of various musical genres; (2) the extent to which downloading trajectories are invariant with respect to countries and/or genres; and (3) possible links between the downloading behaviour of various genre-defined user subgroups and pre-existing music-personality studies. Substantial differences were observed between download trajectories pertaining to pop, rap, rock and metal—metal, in particular, was seen to exaggerate features of rock's trajectory. Of the genres studied, metal was found to have the only invariant trajectory, seemingly impervious to the local conditions of the country in which it was downloaded. Similarly, musical styles within Brazil were found to be downloaded in more or less the same way, in contrast to the UK where download trajectories varied with musical genre. Lastly, the analysis demonstrated a statistical link between pre-existing music-personality research and patterns of downloading within the metadata, suggesting that different downloading behaviours are due, in part, to differences in personality. Au moyen d’une analyse à grande échelle d’une base de données de téléchargement de musique fournie par MixRadio, un fournisseur principal de service de musique en ligne qui appartenait auparavant à Nokia, cet article examine ce qui suit en ce qui concerne la consommation musicale : (1) les différences de trajectoires de téléchargement en ce qui concerne les divers genres de musique; (2) la mesure dans laquelle les trajectoires de téléchargement sont invariantes selon les pays et/ou les genres; et (3) des liens possibles entre le comportement de téléchargement de divers sous-groupes d’utilisateurs dont le genre est défini et les études de personnalité musicale actuelles. Des différences importantes ont été observées entre les trajectoires de téléchargement en ce qui concerne les genres pop, rap, rock et métal — métal, en particulier, a été perçu comme une exagération des caractéristiques de la trajectoire du rock. De tous les genres étudiés, on a constaté que seul le métal avait une trajectoire invariante, apparemment insensible aux conditions locales des pays où il a été téléchargé. De la même façon, on a constaté que les styles musicaux téléchargés au Brésil l’étaient plus ou moins de la même façon, contrairement au Royaume-Uni, où les trajectoires de téléchargement variaient selon le genre musical. Enfin, l’analyse a démontré un lien statistique entre les recherches de personnalité musicale actuelles et les modèles de téléchargement au sein des métadonnées, ce qui suggère que différents comportements de téléchargement sont dus, en partie, aux différences de personnalité.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.189
GPT teacher head0.323
Teacher spread0.134 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it