The concept of source text in audiovisual translation studies: Unexplored implications of “the initial point of view”
Bibliographic record
Abstract
Some audiovisual translation (AVT) scholars redefine the source text (ST) to encompass all modes in the audiovisual text and not just the verbal discourse, as in the still prevalent verbally driven ST notion. Focusing on subtitling, this conceptual paper explores the implications of a holistic redefinition of ST for AVT and TS at large. Two scholarly texts are analysed, both proposing holistic ST concepts but approaching text and translation from different initial points of view—the one starting from whole texts in context and the other from parts of text. The paper examines the scholars’ argumentation regarding their holistic concepts and how it reflects their initial point of view, discussing its significance. The paper suggests, first, that both holistic concepts offer a theoretically more consistent approach to subtitling, compared to the verbally driven one, elucidating, for example, discussions on templates and machine translation in subtitling. Second, the paper argues that AVT provides a strong epistemological argument for a consistent initial whole-to-parts point of view for the study of translation as mediation. It calls for research within the multimodal framework to shift the primary focus from (ST) modes and meaning to the purpose and functions of whole texts in context.
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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.063 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".