Music Genres as Historical Artifacts: The Case of Classical Music
Bibliographic record
Abstract
This article reflects on the use of predetermined genre lists to measure patterns in music taste and, more specifically, classical music taste.Classical music as a whole is in quantitative research typically treated as marker of cultural prestige, although qualitative research suggests great internal diversity within the genre.The use of a predetermined array of genres to measure music taste risks to miss these subdivisions within the classical music genre and thus produces biased results.Therefore, inspired by Lamont's (2010) call to study classification systems 'from the ground up', we present an alternative strategy to measure classical music taste using an open question about artist preferences.We build a two-mode network of classical music artists and respondents and use Infinite Relational Models to identify clusters of respondents that have similar relationships to the same set of artists.We detect no less than five distinct listening patterns within the classical music genre.Two of these preference clusters focus only on very central, popular classical artists.Another cluster combines these popular artists with more contemporary artists.One cluster focuses on only one very accessible artist and, finally, there is a cluster of respondents that distinct themselves by having a real connoisseur taste.Furthermore, we find that expert taste in classical music is not related to social distinction.Instead, knowledge of the most central and popular artists (e.g.Bach, Beethoven, Mozart) is typical for respondents with a high socio-economic background.Social distinction seems more related to knowledge of popular artists in classical music than to distinctive, connoisseur taste.Our findings show the potential of social network analysis for the problem of music taste classification and cultural sociology in general.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".