Traduction française de récits autochtones du Canada comportant des mots empruntés aux langues indigènes
Bibliographic record
Abstract
Canada's Native literature is rooted in an oral tradition, like many other aboriginal literatures. This literature's main characteristics are often distinct from those of the Western literary tradition. Throughout years and history, scholars and theorists from English Canada and Quebec have more or less ignored the members of the First Nations of this country, and their literature. However, since the late sixties, we have been witnessing a great revival of Native writing in Canada, which suggests a better and stronger image of the Native protagonist. Consequently, the contribution and image of Native literature on the English-Canadian and Quebec literary scenes have changed. The French translation of the literary works of Native authors contributes to the survival of this often marginalized literature. This transfer from one language to another implies, in this particular case, the keeping of borrowings from various Native languages used in the story. This is the central element of this M.A. thesis. The texts that I have here chosen to translate from English to French--stories from Brian Maracle, Basil Johnston, Alexander Wolfe and Marion Tuu'luq--originate from oral traditions, and all contain Native words. The same applies to their French translations, which adopt a"foreignizing" strategy by keeping those Native words in the target text. In order to help readers understand them, the authors have used various strategies which were maintained in the French translations. In addition, the specific rhythmic and syntactical characteristics of the source language, derived from the oral tradition of Native storytelling, have all been taken into account and explained in relation with translation studies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".