Anti-Colonial Strategies in Cross-cultural Music Science Research
Bibliographic record
Abstract
ethical and methodological issues within cross-cultural music science research, including issues around community based research, participation, and data sovereignty. Although such issues have long been discussed in social science fields including anthropology and ethnomusicology, psychology and music cognition are only beginning to take them into serious consideration. This paper aims to fill that gap in the literature, and draw attention to the necessity of critically considering how implicit cultural biases and pure positivist approaches can mar scientific investigations of music, especially in a cross-cultural context. We focus initially on two previous papers (Jacoby et al., 2020; Savage et al., 2021) before broadening our discussion to critique and provide alternatives to scientific approaches that support assimilation, extractvism, and universalism. We then discuss methodological considerations around cross-cultural research ethics, data ownership, and open science and reproducibility. Throughout our critique, we offer many personal recommendations to cross-cultural music researchers, and suggest a few larger systemic changes.
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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 | 0.130 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.077 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".