Bibliographic record
Abstract
This issue of TranscUlturAl includes a variety of articles that reflect how rich and diverse the theme of translation and performance is because they draw from various disciplines, such as music, theatre, comics, poetry, and image studies.While translation itself, of course, can be viewed as performative the aim of this collection is primarily to explore what happens to translation when it is involved in performance.Since performing arts are multimodal, the translator must pay attention to the nonverbal dimensions of a performance, such as gestures, facial expressions, images, movements, visual perspectives, sounds, timing or lighting, in addition to language.In this way, opera translation involves more than libretti and surtitles, song translation more than poetry, drama translation more than dialogue, and film translation more than the constraints dictated by the choice of dubbing or subtitling.The text on which a performance is based does not stand alone since its meaning only achieves its full potential through the performer's interpretation of it.For translators this presents a huge challenge because no matter how apt their lexical choices or how clever their turns of phrase, they always need to consider how suitable those are for the performer.Indeed, the very best textual translation of a song may prove to be unusable if the singer discovers that the words do not match the notes because, for example, the natural accentuation of a particular language interferes with the rhythm of the music.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.141 | 0.078 |
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".