Bibliographic record
Abstract
Purpose How does one classify instructional videos uploaded by musicians of different caliber and mastery on video‐sharing sites? What kinds of communities are forming around these content sources? How does one address the different perception and understanding of what music means to a diverse audience? How does one identify and address the needs of new kinds of users, who learn how to play music by using primarily online resources? While this paper does not seek to directly address all these questions, it aims to raise them with the aim of contextualizing the discussion as a necessary foundation to effectively address the more practical questions above. Design/methodology/approach This paper presents a knowledge organization model of music knowledge based on the concept of musicianship as used in music education. A balanced and holistic approach is sought, especially in light of the interdisciplinary nature of the challenge being addressed. Drawing on Hjørland's work on domain analysis, and Hennion's concept of the user of music, this paper discusses music as a domain, music as information, and music as knowledge. Findings In particular, the concept of listening and genre are considered important ways through which one mediates one's understanding of music as knowledge. There are four “layers” in the model: Vocabulary of Music; Structures and Patterns of Music; Appreciation of Music; and Cultural‐Historical Contexts. Originality/value The model addresses knowledge organization challenges specific to the domain of 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".