“First Week Is Editorial, Second Week Is Algorithmic”: Platform Gatekeepers and the Platformization of Music Curation
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 it