Community Collaborative Participatory Archive (CCPA): Towards a New Archival Practice in Ethnomusicology
Bibliographic record
Abstract
Abstract This article addresses the gaps between ethnographic archives and community members who are often deprived of accessing their own materials. In reflecting on results from collaborative research with a Nepalese immigrant community in Alberta, Canada, where we created a Digital Community Archive (DCA), I draw attention to the benefits of combining strategies from applied ethnomusicology and Participatory Action Research (PAR). I propose a new model for archiving in ethnomusicology, the Community Collaborative Participatory Archive (CCPA). This model can improve ethnomusicological archival practice by focusing on collaborative, egalitarian, and grassroots participation, shared roles, and authority in the archival creation and development process.
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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 | 0.064 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.065 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".