Subtitling Language Diversity in Spanish Immigration Films
Bibliographic record
Abstract
In Spain, the growing number of films depicting characters in multicultural settings bears testimony to the demographic changes experienced by Spanish society since the late 1980s. From a translational point of view, these films attract attention of researchers because of the presence of immigrant characters that use their mother tongue in addition to the language(s) of their host society. In this paper we present the results of the second stage of a research on the linguistic diversity in Spanish films starring immigrants. While the first stage dealt with the original audiovisual texts, we focus on their subtitled versions in two European languages. To do so, a descriptive and empirical methodology has been followed, the first step of which was the creation of a thorough corpus of six Spanish films and their corresponding eight target versions (in English and French). The descriptive and microtextual analysis of the immigrants’ dialogues found in our corpus allows us to define the translation strategies and techniques employed by subtitlers. Then, these techniques are classified in a continuum according to their degree of domestication and foreignisation. Finally, some conclusions are drawn regarding the ideology behind the cinematographic reflection of immigrants’ foreignness.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".