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Record W2134404195 · doi:10.1287/isre.1110.0405

<b>Research Note</b>—Music Blogging, Online Sampling, and the Long Tail

2012· article· en· W2134404195 on OpenAlex

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.

Bibliographic record

VenueInformation Systems Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsMcGill University
Fundersnot available
KeywordsPopularityNews aggregatorSampling (signal processing)AdvertisingSocial mediaMusic industryConsumption (sociology)Popular musicLong tailComputer scienceBusinessSociologyPsychologyWorld Wide WebMusic educationArtStatisticsVisual artsSocial psychologyTelecommunicationsMathematicsSocial science

Abstract

fetched live from OpenAlex

Online social media such as blogs are transforming how consumers make consumption decisions, and the music industry is at the forefront of this revolution. Based on data from a leading music blog aggregator, we analyze the relationship between music blogging and full-track sampling, drawing on theories of online social interaction. Our results suggest that intensity of music sampling is positively associated with the popularity of a blog among previous consumers and that this association is stronger in the tail than in the body of music sales distribution. At the same time, the incremental effect of music popularity on sampling is also stronger in the tail relative to the body. In the last part of the paper, we discuss the implications of our results for music sales and potential long-tailing of music sampling and sales. Put together, our analysis sheds new light on how social media are reshaping music sharing and consumption.

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)0.043
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.199
GPT teacher head0.468
Teacher spread0.269 · 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