“First Week Is Editorial, Second Week Is Algorithmic”: Platform Gatekeepers and the Platformization of Music Curation
Bibliographic record
Abstract
This article investigates the logics that underpin music curation, and particularly the work of music curators, working at digital music streaming platforms. Based on ethnographic research that combines participant observation and a set of interviews with key informants, the article questions the relationship between algorithmic and human curation and the specific workings of music curation as a form of platform gatekeeping. We argue that music streaming platforms in combining proprietary algorithms and human curators constitute the “new gatekeepers” in an industry previously dominated by human intermediaries such as radio programmers, journalists, and other experts. The article suggests understanding this gatekeeping activity as a form of “algo-torial power” that has the ability to set the “listening agendas” of global music consumers. While the power of traditional gatekeepers was mainly of an editorial nature, albeit data had some relevance in orienting their choices, the power of platform gatekepeers is an editorial power “augmented” and enhanced by algorithms and big data. Platform gatekeepers have more data, more tools to manage and to make sense of these data, and thus more power than their predecessors. Platformization of music curation then consists of a data-intense gatekeeping activity, based on different mixes of algo-torial logics, that produces new regimes of visibility. This makes the platform capitalistic model potentially more efficient than industrial capitalism in transforming audience attention into data and data into commodities.
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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 | 0.026 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".