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Record W2990895295 · doi:10.5281/zenodo.3527912

Tunes Together: Perception and Experience of Collaborative Playlists

2019· article· en· W2990895295 on OpenAlexaff
So Yeon Park, Audrey Laplante, Jin Ha Lee, Blair Kaneshiro

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPerceptionComputer scienceMultimediaHuman–computer interactionPsychology

Abstract

fetched live from OpenAlex

Music is well established as a means of social connection. In the age of streaming platforms, personalized playlists and recommendations are popular topics in music information retrieval. We bring the focus of music enjoyment back to social connection and examine how technologies can enhance interpersonal relationships, specifically through the context of the collaborative playlist (CP). We conducted an exploratory study of CP users and non-users (N=65) and examined speculative and experienced purposes and outcomes of CPs, as well as general perspectives on music and social connectedness. We derived a CP Framework with three purposes - Practical, Cognitive, and Social - and two connotations - Utility and Orientation. Both users and non-users shared similar perspectives on music-related activities and CP user outcomes. Projected and actual CP purposes differed between groups, however, as did perception of music's role in connectedness in recent years. These results highlight the importance of music-based social interactions for both groups.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.023
GPT teacher head0.279
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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