Musical Similarity as Conceived by “Avid Recreational Music Listeners”
Bibliographic record
Abstract
Over the past century, sociocultural and technological developments have fostered the emergence of what Peterson and Kern (1996) call “omnivorous” music listeners, who listen to music from a variety of different genres. As well, non-hierarchical forms of categorization, such as tagging, have appeared in recent years. Despite such trends, genre remains the primary basis for categorizing music in systems with content, metadata, or both. Furthermore, techniques employed within many recommender systems, intended to aid listeners with finding music for recreational listening, indirectly continue to reflect genre-based categorization and taste. This paper provides an overview of the contexts in which such trends have emerged. It also considers prospects for incorporating actively nuanced dimensions of similarity into recommender systems, which could enable users to engage in cross-genre music discovery more easily than current systems allow. To provide further grounding for such possibilities, I am currently conducting a study to determine how “avid recreational music listeners” conceptualize musical similarity. This paper discusses the study’s methodology, which consists of semi-structured interviews and music-seeking exercises.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".