Bibliographic record
Abstract
The article addresses the problem of constraints typical of opera surtitling, an audiovisual translation modality that seems rather neglected as far as the academic discourse is concerned. Although the termconstraintmay appear to have mainly negative connotations, it seems that the idea of a constraint may often prove helpful, since it may facilitate the process of translation by restricting the scope of possibilities and hence justify the chosen techniques. The article is meant to propose a classification of potential constraints on the surtitling process, including the constraint of a live performance, music, audience design or relevance, and the resulting implications for the whole process. It is argued that the awareness of the constraints operating in the process of drafting surtitles helps to understand the rationale behind this particular translation activity and consequently helps to draft good quality surtitles which serve their original purpose.
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 | 0.023 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".