Bandcamp, SoundCloud, and the Digital Underground: \nExploring Curatorial Practice Across Independent Music Platforms
Bibliographic record
Abstract
The modern electronic dance music DJ has become somewhat of a ubiquitous figure in popular culture, though few efforts have been made to better understand the curatorial dimension of their craft. This thesis responds to the question of how exactly digital music platforms have come to define curatorial practice, and in what ways have they shaped the role of the contemporary dance music DJ. I began by conducting platform analyses of Bandcamp and SoundCloud, in which I examined how their user interfaces, social affordances, and approaches to music categorization relate to certain ideas of musical habitus, curating with care, and networked curation. I then conducted interviews with two DJs from the local Montreal electronic music scene. These interviews consisted of a recording session where DJs were asked to perform one hour long mix, and then a discussion period in which they were asked to reflect on their curatorial voice, preparation methods, and mixing techniques. The recorded mixes act as an auditory accompaniment to my thesis and can be accessed digitally alongside this text. Ultimately, I found that though these platforms may steer curators one way or another by nature of their user interface or integrated algorithms, they are not necessarily shaping the innate qualities of curation. DJs still rely on community connections, active knowledge sharing, and affective responses to music to guide their curation. Instead, platforms like Bandcamp and SoundCloud are most valuable when understood as tools that enhance our ability to curate with care and expand on our pre-existing musical habitus.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".