Orality Markers in Spanish Native and Dubbed Sitcoms: Pretended Spontaneity and Prefabricated Orality
Bibliographic record
Abstract
This article reflects on the divergences between translated and non-translated texts, and the specificities of fictional dialogue in audiovisual texts. Research suggests that audiovisual dialogue consists of a combination of linguistic features used in speech and writing, and that both translators and scriptwriters should aim to achieve a balance of these features to create spontaneous-sounding dialogues. Working with an audiovisual corpus of domestic and dubbed sitcoms in Spanish ( Siete Vidas and Friends respectively), the purpose of this article is to provide an overview of how Spanish dialogues are shaped from a linguistic point of view across all language levels, highlighting the trends identified in their production and their translation in order to compare them. The results reveal the complex relationship established between speech and writing in audiovisual texts, and disclose the resources used by translators and scriptwriters to carefully plan dialogues which sound credible. Findings also suggest that domestic audiovisual texts bear more resemblance to spontaneous conversation than dubbed texts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".