Download or stream? Steal or buy? Developing a typology of today's music consumer
Bibliographic record
Abstract
Abstract This paper explores the impact that recent transformations in digital music technology (e.g. the increasing popularity of legal streaming platforms) have had on the consumer experience. Following 35 in‐depth qualitative interviews, we have identified four key segments of contemporary music consumers (steadfast pirates, ex‐downloaders, mixed tapes and the old schoolers [the disengaged]) based on a continuum of their preference for illegal music piracy. Examining key themes (e.g. morality, format, value and identity investment) to distinguish each segment, we contribute to a fragmented music piracy literature in particular through the identification of the “ex‐downloaders” and “mixed tape” segments. Previous literature has tended to frame music piracy in very simplistic terms, failing to acknowledge a large number of consumers who are conflicted about their actions and rationalise their piracy in complicated and inconsistent ways related to the broader industry and their own sense of identity as a music consumer. Additionally, the discussion of the ex‐downloader segment provides significant evidence that for a large number of consumers, a policy of participation, in the shape of providing superior alternatives for legal digital music consumption, can be much more beneficial in tackling the problem of piracy than previous strategies of policing and coercion. Managerial and future research implications are discussed. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".