Writing Sound Into the Wind. How Score Technologies Affect Our Musicking
Bibliographic record
Abstract
In this slightly updated text of his keynote speech at the Annual Congress 2019 of the GMTH, Sandeep Bhagwati discusses foundational concepts of current discourses on notation, such as notational perspective and comprovisation. He elaborates on the place of notation in an ongoing evolution that sees sound production gradually move away from human agency and its translation into the visual and unfolds the field for possible notation opened up by new sensory technologies. Will the introduction of such responsive and fluid score technologies once more change the very nature of what we call music? Finally, he imagines possible shifts in the ontology of musicking that may be occasioned by such ‘invisible’ notations and through non-human agency in musicking. In diesem leicht überarbeiteten Text seiner Keynote auf dem Jahreskongress 2019 der GMTH erörtert Sandeep Bhagwati grundlegende Konzepte des aktuellen Notationsdiskurses, wie z. B. notational perspective und comprovisation. Er erläutert den Platz von Notationsformen innerhalb einer laufenden Entwicklung, in der sich die Klangerzeugung allmählich vom menschlichen Handeln und seiner Übersetzung ins Visuelle entfernt, und entfaltet das Feld möglicher Notationen, das durch neue sensorische Technologien eröffnet wird. Wird die Einführung solcher reaktionsfähiger und fließender Notationstechnologien die Natur dessen, was wir Musik nennen, erneut verändern? Schließlich stellt er sich mögliche Verschiebungen in der Ontologie des Musizierens vor, die durch derartige ›unsichtbare‹ Notationen und durch nichtmenschliches Handeln im Bereich des Musizierens hervorgerufen werden könnten.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".