Film Music Cues: Visualizing Social Reality Through Music and Film
Bibliographic record
Abstract
Taxonomies may contain functional vocabulary and display relationships among concepts to facilitate the work of a domain. Silent film music is a work-based genre of musical performance. Musicians worked from cue-sheets of musical terms. This paper describes the conversion of a working list of musical cues into a taxonomy. Results show the taxonomical differences that arise from a work-based vocabulary. Also, the social realities of the time are reflected in this vocabulary of music for silent film from the 1920s. Les taxonomies peuvent contenir du vocabulaire fonctionnel et mettre en évidence des relations entre les concepts et ainsi faciliter le travail d'un domaine. La musique de films muets est un genre de performance musicale basé sur l’expérience. Les musiciens travaillent à partir de repères («cue-sheet») de termes musicaux. Cet article décrit la conversion d'une liste de repères musicaux en une taxonomie. Les résultats montrent les différences taxonomiques qui émergent d'un vocabulaire basé sur l’expérience. En outre, les réalités sociales de l'époque sont reflétées dans ce vocabulaire musical pour le cinéma muet des années 1920.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".