Influence d’une langue autochtone et de la littérature orale dans <i>Ravensong</i> de Lee Maracle et sa traduction
Bibliographic record
Abstract
La plupart des autrices et auteurs autochtones en Amérique du Nord publient en anglais, ce qui n’empêche pas certains d’entre eux de s’approprier la langue héritée du colonialisme et de lui imposer l’influence d’une langue autochtone. L’écriture de nombre d’entre eux reflète également l’influence des littératures orales. On peut constater ces deux spécificités dans une oeuvre comme Ravensong (1993) de l’écrivaine stó:lōe-salishe Lee Maracle. Cette dernière a d’ailleurs insisté sur l’importance de l’oralité dans sa culture et dans son propre rapport aux récits. De plus, Maracle a fait des entorses à la grammaire anglaise, entre autres en féminisant certains termes anglais. Comment la traduction de Joanie Demers, pour le compte de la maison d’édition québécoise Mémoire d’encrier, a‑t‑elle réussi à rendre justice à ces caractéristiques de l’écriture de Maracle ?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".