Naturalness in the Spanish Dubbing Language: a Case of Not-so-close Friends1
Bibliographic record
Abstract
The present article examines the Spanish dubbing language from the point of view of its naturalness. The premise is that dubbing language is best analyzed by comparing it to the register it imitates, as long as its peculiar features are taken into consideration. This study is divided into two parts: firstly, a description of the features that make dubbing dialogue different from real dialogue, focusing on those arising from the source text; secondly, a comparative analysis of dubbed and real dialogue. In the latter, a corpus of spontaneous conversations will be used as a yardstick for natural dialogue and the main strategies used in colloquial conversation will provide the linguistic units to be analyzed: intensifiers and discourse markers. The main unidiomatic features detected are the use of anglicisms, especially at the pragmatic level, and a certain shift in tone that may cause a variation in the relation among the participants in the dubbed text. Finally, the notion of suspension of linguistic disbelief is put forward as a possible explanation for the perpetuation of unnatural features in dubbing 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".