“I will survive”: Online streaming and the chart survival of music tracks
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
Digital streaming has had a profound effect on the commercial music sector and now accounts for 80% of industry revenues in the United States. This study investigates the consumption of music on digital streaming platforms by analyzing the factors affecting the chart survival of individual music tracks. Our data are taken from the Spotify Global Top 200 between January 2017 and January 2020, containing observations on 3,007 unique tracks by 642 artists over 1,087 days. We identify a number of unique consumption traits applicable to online streaming services, which we use to explain variations in chart longevity. We find a positive association between the amount of time a track spends in the chart and the involvement of a major label. We also find that the level of competition from other chart entries, as well as some elements related to the pattern of diffusion, associates significantly with the likelihood of chart survival. The study highlights several important managerial implications for key industry stakeholders.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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