Response to “Global Notation as a Tool for Cross-Cultural and Comparative Music Analysis”
Bibliographic record
Abstract
The editors of Analytical Approaches to World Music are pleased to present the following set of invited responses to Andrew Killick’s article “Global Notation as a Tool for Cross-Cultural and Comparative Music Analysis.” While Andrew Killick has been developing global notation since 2016, the present article stems from his presentation at the Fifth International Conference on Analytical Approaches to World Music in Thessaloniki in 2018. The presentation generated lively discussion at the conference, and when Killick subsequently submitted the paper to the journal, one of the peer reviewers recommended publishing responses alongside the article. In compiling these responses, we have sought to include perspectives from scholars with a range of backgrounds and identities in terms of academic discipline, area of expertise, race, gender, age, and nationality. The set of responses does not fully live up to these goals insofar as the majority of respondents are men, are white, and are based in the US, Canada, or the UK. The fact that most of the women and Black, Asian, and Latinx scholars whom we invited were not able to spare the time to contribute responses may partly reflect the pervasive inequities within and beyond academia that have only been deepened by the ongoing public health crisis. In any case, our failure to attract a more diverse group of respondents should not reflect on the responses themselves, which offer thought-provoking and creative commentaries on the promise and challenges of Killick’s innovation to music notation, and we are grateful to all respondents for their contributions to the project. The responses are linked below in alphabetical order by author’s name.
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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 | 0.025 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.022 | 0.035 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".