The Thematic Representation of Female Composers in Classical Music on Streaming Platforms
Bibliographic record
Abstract
Women have been increasingly fighting for their rights and equality in Society. Historically, in the 18th century, women’s musical education was considered a distraction and participation in family events, but professional engagement was not deemed acceptable. Given this context, questions arose about the existence of female classical music composers and whether their works are present on current music streaming platforms. Based on this, the study aimed to investigate how these composers are thematically represented on these platforms. Methodologically, the study is qualitative and exploratory, employing case study methods. The search terms used were “woman” and “classical music” along with the composers' names. The criterion for selecting composers was that they belonged to the 18th, 19th, and 20th centuries, as many male composers achieved enduring success during this period. The selected composers were Maria Anna Mozart, Fanny Mendelssohn, Clara Schumann, Chiquinha Gonzaga, and Alma Mahler-Werfel. The chosen platforms were Spotify, Amazon Music, Deezer, and Apple Music. The research demonstrated a scarcity of historical records about the analyzed composers’ lives. They were often compelled to abandon music due to family obligations, which contributed to the limited availability of their music on the platform.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".