La Place du Surtitrage Comme Mode de Traduction et Vecteur D'échange Culturel pour les Arts de la Scéne
Bibliographic record
Abstract
Despite the fact that surtitling is a rapidly expanding field which enables plays and operas to be understood by audiences in different countries unfamiliar with the original language, this research area is quite new and relatively unexplored. The question of how the actual verbal and non-verbal material is structured in surtitling has received only minimal analysis in scholarly terms so far. Very little has been written on the reasons for reduction methods. Up to now scholars seem not to have considered the new context which surtitling offers for an intertextual approach to translations. This study starts from the idea that surtitling is a translation constrained by its context, by time and space as much as by culture and the multi-semiotic levels of the performance. Opera, puppet theatre and drama are not only made of text; they involve visual and musical signs which may be looked at in a new light, from an inter-artistic semiotic perspective. Surtitling enables cultural exchanges; further investigation of its role as such is not only needed, but makes it one of several interesting new fields of research in translation and intercultural studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".