Collaborative Playlists around the World: A Cross‑Cultural User Study
Bibliographic record
Abstract
Collaborative playlists (CPs) enable users of streaming platforms to share and discover music through co‑curation. Recent studies involving predominantly North American samples have found that CPs are created for a variety of contexts, help users organize and access music, facilitate music discovery, and support social connections. Yet, despite these important benefits, little is known about how CP usage aligns or varies across different cultures. We conducted an exploratory study to better understand the landscape of collaborative music engagement with a focus on Hong Kong, South Korea, Quebec, and the United States. We found that across these cultures, previously established purposes for engaging in CPs apply, yet with different degrees of emphasis. Perceived and expected CP outcomes and broader perspectives on social connection through music also varied by location and CP user type. With these findings we discuss primary similarities and differences across the studied cultures and highlight directions for future investigations to further elucidate how music platforms with CP functionalities—and social capabilities more generally—can better help users achieve their desired goals around music.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".