Bibliographic record
Abstract
Recent research in audiovisual translation has focussed on the language of both original and translated dialogue, revealing different degrees of alignment between fictional dialogue and spontaneous conversation. In this context, demonstratives deserve special attention as they are major means to highlight segments of the current discourse and extra-linguistic reality in speech and may play a significant role in cinematic language as well. Furthermore, demonstratives are an area of dissimilarity between languages, with their translation being potentially subject to interference from the source to the target text. Through a quantitative corpus-based approach, this study explores to what extent demonstratives occur in the language of Italian dubbing, how similar in this respect dubbed dialogue is to Italian spoken language and what translation operations may account for the observed translation outcomes. Drawing on a small English-Italian parallel corpus of film dialogue, all English demonstrative pronouns have been coded for syntactic role, pragmatic function and translation operation. Results show that demonstratives occur to a lesser extent in dubbed film language vis-à-vis both Italian conversation and the source English dialogues. These findings are discussed in terms of the cross-linguistic contrast between Italian and English as well as the convergence of dubbed dialogue towards the model of original Italian film language.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".