Musik, Kontext, Wissenschaft : interdisziplinäre Forschung zu Musik = Musiques, contextes, savoirs : perspectives interdisciplinaires sur la musique
Bibliographic record
Abstract
In den letzten Jahren hat der interdisziplinare aber auch internationale Austausch zum Forschungsobjekt Musik neue methodische Herangehensweisen und Untersuchungsgegenstande eroeffnet. Der Band versammelt Beitrage aus dem deutschen und franzoesischen Forschungskontext mit einem Themenspektrum von Opernparodien des 18. Jahrhunderts uber die Musik in kanadischen Gefangenenlagern bis hin zur vusic junger Gehoerloser. Entlang der zentralen Begriffe Identitat, Historiographie, Erfahrung und Praxis zeugen sie von den gegenwartigen Perspektiven auf Musik in all ihren technischen, kulturellen und soziohistorischen Facetten. Au cours des dernieres annees, les echanges interdisciplinaires mais aussi internationaux autour de l'objet musique ont fait emerger de nouvelles perspectives methodologiques et de nouveaux terrains d'etude. Cet ouvrage rassemble les contributions de chercheurs d'Allemagne et de France travaillant sur un large spectre de themes, allant des parodies d'opera du 18e siede aux pratiques musicales de prisonniers de guerre au Canada, en passant par la vusic de jeunes sourds. A partir des concepts centraux d'identite, d'historiographie, d'experience et d'action musicienne, ces travaux actuels rendent compte des diverses facettes du fait musical, envisage dans ses dimensions techniques, culturelles et socio-historiques.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".