Creative subtitling as film-transformative practice: From immersion to amazement in Edgar Pêra’s <i>The Baron</i>
Bibliographic record
Abstract
In this article, I examine creative subtitling as a cultural practice whose significance exceeds its use value as merely an enhanced variety of subtitling. The article’s main argument is that creative subtitling transforms the films that contain it by expanding further on their creative ideas, by foregrounding these films’ constructedness, and by embedding new layers of self-awareness in them. I begin the analysis by critiquing some recent studies on creative subtitling for downplaying the part of creativity in favour of a reductive conceptualisation of translation as meaning transfer. I then turn to Antoine Berman’s account of translation as reflexive practice, the ethical aim of which is to undo this conceptualisation and turn our attention away from meaning and closer to the experience of language itself. Taking translational creativity to mean literally this transformative “undoing,” in the last section, I examine the case of the creative subtitles of Edgar Pêra’s film, O barão [ The Baron ] (2011). I argue that these subtitles expand further on the creative ideas of this film while simultaneously “undoing” it: they take us out of a state of immersion in the story and into a state of “amazement” at the experience of the language of cinema itself.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".