Serendipity on radio and streaming: Between musical discovery and recognition
Bibliographic record
Abstract
This article seeks to discuss, within the scope of radio and audio media studies, how serendipity articulates discovery, memory and recognition, standing at the heart of current Music Streaming Platforms’ rhetoric and musical culture. Fortuitous discovery mobilizes affections and helps to build bonds (either with platforms or radio stations), representing a major role in music innovation and circulation. Combining a range of current Global South references with critical theories on media and memory, we address similarities and differences between music programming and curation on radio and streaming, refuting computer scientists’ ambition to engineer serendipitous experiences and highlighting that serendipity must be correlated with the listeners’ sociocultural background. We conclude that acknowledging the complexity of serendipity opens doors for thinking of critical issues concerning musical consumption, conditions of listening, identity, representation and audio media agency. These are central themes in restructuring of the music industry, in a context of platformization.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".