La traduction audiovisuelle
Bibliographic record
Abstract
The present study is intended primarily as a contribution to translation teaching. It deals with audiovisual translation as a process of intercultural mediation (Guillot and Pavesi 2019), in particular with the potential of translated televisual products addressed to a younger audience to promote the acceptance of diversity through a combination of translation strategies. It focuses on a recent type of audiovisual product – i.e., multiethnic/multicultural situation comedies for tween-age audiences, or ‘kidcoms’ (Manfredi 2018, 2021) – suggesting that this might represent valuable material for activities of translational analysis and practice within a University translation course. \nA Canadian sitcom, i.e How to Be Indie (Santamaria, May & Bolch 2009-2011), dubbed into different languages, including Italian as Essere Indie (2010-2012), is offered as a case in point. Its episodes revolve around the adventures of a thirteen-year-old girl, Indira (nicknamed Indie), and family, emigrated to Canada from India, and centre around the issue of cultural diversity. \nThe identification of macro- and micro- translation strategies that convey the linguistic and cultural representation of characters’ identities is proposed as a classroom activity aimed at highlighting that a (dubbing) translator has the possibility of acting as an intercultural mediator, with a double goal: on the one hand, to permit the intercultural exchange between different languages, texts and contexts and, on the other hand, to safeguard linguistic and cultural diversity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.126 | 0.043 |
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".