Contemporary Notation Framework: Approaches, encoding and medium
Bibliographic record
Abstract
Abstract Since the mid-20th century, approaches to musical notation have multiplied, giving rise to a multitude of terminologies and classifications. While there exists an extremely rich literature on new approaches to musical notation, it is easy to be confused by a nomenclature that is still under construction and has yet to be formalised. Based on a narrative review of the scientific literature comprising over 250 documents on new forms of notations, this article aims to present the main terminologies used to describe the different approaches to notation. This article proposes a framework illustrating what we observed as the most prominent notation approaches (action-based scores, animated scores, graphic scores, etc.) according to the types of indications (prescriptive and/or descriptive), the notation encoding (semantic and temporal encoding), and the mediums used for transmission (screen, printed, etc.). The contemporary notation framework aims to provide tools for the further analysis and classification of musical notation used in contemporary instrumental, electronic, and electroacoustic music.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".