A multimodal model of analysis for the translation of songs from stage musicals
Bibliographic record
Abstract
Song translation has only recently become an area of interest for research purposes, with the development of studies on opera, films, folk music, cover songs, and more. Not many scholars have researched stage musicals, even though songs play a fundamental role in this type of performance, conveying meaning via verbal, audio and visual semiotic resources. A few studies on song translation can be identified, such as Low’s (2003; 2005) Pentathlon Approach and Franzon’s (2005) functional approach. These models of translation offer valuable guidelines on how to treat the lyrics, but what is missing is a systematic and multimodal model of analysis that can be applied to the song in its entirety. Kaindl (2005; 2013) takes into consideration the multimodality of songs, but only focuses on popular music and opera. Acknowledging the lack of substantial research on the interaction between modes, which is typical of stage musicals, this paper focuses on the development of a model of analysis that considers the semiotic complexity of songs. A new approach based on themes will allow for a more holistic view of the song and of its content.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".