What is Gained in Subtitling: How Film Subtitles Can Expand the Source Text
Bibliographic record
Abstract
The problem of translation and loss is a cardinal concern in translation studies. Conventional wisdom tells us that translation must necessarily entail loss. However, some translation studies scholars have argued that translation can yield significant originality in the target text. Christiane Nord, for one, argues that literary translators can claim authorial presence by actually causing the source text to “grow” in a way that is quantitative and qualitative. Although Nord’s idea applies mainly to literary translation, it raises questions about how this could apply to translations of other types of creative source texts, such as audio/visual translation. The format of interlingual subtitling between two disparate languages, such as English and Japanese, burdens translation with severe constraints and considerable loss text is taken for granted. But what is lost? Meaning? Nuance? This paper argues that these need not be lost in subtitling. In fact, by applying Nord’s model of source text growth to subtitling, we can see how subtitling produces new value to the source text. Through a close analysis of the Japanese subtitles of the 2007 film, There Will Be Blood, this paper will demonstrate that despite the severe constraints placed on the translation found in film subtitling, subtitles can promote “qualitative growth” by transferring the poetic function of the source text into new configurations in the target text, prompting target text viewers to interpret content in new ways.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".