Analysis from multiple perspectives (AMP): Applying decision hygiene to analysis of musical structure
Bibliographic record
Abstract
Music analysis is a complex and subjective task requiring a considerable degree of judgment on questions often lacking verifiable answers. In many cases, this subjectivity leads to seemingly intractable disagreements. Although disagreements can offer useful insight, whether they represent genuine differences in perspective is not always clear. To contribute to this challenging aspect of music analysis, here we introduce a procedure inspired by recommendations for improving decision making in other domains lacking verifiable answers, such as judicial sentencing. Our approach involves a 3-phase procedure, combining independent analyses with information sharing and re-evaluation among five graduate-level music analysts. We show that this procedure reduces self-identified errors/oversights in music analysis while preserving meaningful differences in perspective. As a proof of concept, we apply this procedure to 381 excerpts from 16 historic sets of preludes to assess relative mode , a complex musical property alluded to in previous scholarship but never formally explored in theoretical applications. This procedure (yielding complementary qualitative and quantitative data) demonstrates how a new, group-based approach to music analysis can offer insights unavailable from more traditional single-scholar approaches.
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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 | 0.065 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".