Przekład jako narzędzie pojednania? O tłumaczeniu kanadyjskich literatur rdzennych na język francuski w dobie dekolonizacji
Bibliographic record
Abstract
Translation as a Tool for Reconciliation? Translation of Canadian Indigenous Literatures into French in the era of Decolonization The common assumption is that translation helps to share ideas and build bridges between societies, cultures and languages. Nevertheless, in Canadian history translation has been a tool of colonial domination and oppression of indigenous communities as well as francophone minorities scattered across Canada after 1763. In view of the above, this paper aims to show from a translational point of view the attempts to redress decades of persecution and assimilation that are currently taking place, particularly in light of the findings and recommendations of the Truth and Reconciliation Commission released in June 2015. The analysis covers a unique context, involving the translation of works of Indigenous Literature into a minority language such as French in Canada. After presenting current trends in this area, the paper will discuss an autobiographical novel Halfbreed by Metis author Maria Campbell, which appeared in French translation in 2021 almost half a century after the original was published.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".