Curating Ethnomusicology in Cyberworlds for Ethnomusicological Research
Bibliographic record
Abstract
We describe a musical cyberworld as a virtual space for curating ethnomusicology, as well as for conducting research: the ethnomusicology of controlled musical cyberspaces. Our cyberworld differs from most online music curation in enabling immersive, social experience. Considering such cyber-exhibition of ethnomusicological research as itself a form of social and musical practice also calls for an ethnomusicology of such exhibits. Research in ethnomusicology has typically been conducted through qualitative fieldwork in uncontrolled settings. By contrast, we design a custom musical cyberworld as a virtual ethnomusicological laboratory, a platform for research geared towards better ways of designing online musical exhibitions for discovery, learning, and aesthetic contemplation, as well as contributing towards our general understanding of the role of music in human interaction and community formation.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".