Il Nunavut dalla pagina allo schermo: la traduzione audiovisiva di The Snow Walker di Farley Mowat
Bibliographic record
Abstract
This essay takes as its starting points the notion of remediation and the linguistics of subtitling in order to advance a new reading of the audiovisual translation of Farley Mowat’s short story Walk Well, My Brother (1975), whose remediation in film The Snow Walker (2003) is a paramount example of foreignising translation aimed at protecting the ethno-cultural diversity in Canada. Not only such an Inuit film with subtitles as The Snow Walker envisions the clash between Canadian and Inuit cultures but is also a survival tale in the far North examining the relationship between technology and minority cultures. I track through these references and look at the issues – the role of subtitling in the preservation of cultural specificity, subtitling strategies for rendering culture-bound terms, cohesion and coherence in subtitling, segmentation, etc. – which they raise. But my central purpose is to re-read the aforementioned subtitled film by applying the linguistics of subtitling and its text-reduction shifts. I analyse the problems of rendering intra-linguistic and extra-linguistic cultural elements from one language and cultural into another in order to demonstrate the challenge of rendering hybrid forms or multilingualism. Through The Snow Walker, I suggest, subtitling may be considered as an extreme form of foreignisation, a modality which is able to conceptualise cultural diversity thereby avoiding ethnocentric violence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".