Réflexion autour de deux stratégies de traduction s'appuyant sur un récit poétique de type postcolonial, Wenjack, de Joseph Boyden
Bibliographic record
Abstract
Ce mémoire est une réflexion sur l'acte traductif à travers l'étude de la traduction littéraire et de la traduction postcoloniale. Il a pour but d'étudier le travail du traducteur en amont de la traduction et pendant l'acte traductif, ainsi que les effets de ses différents choix sur le texte et sur les lecteurs. A cette fin, deux versions de traduction d'un passage du roman Wenjack, écrit par Joseph Boyden en 2016, y sont proposées par la traductrice et analysées par quatorze lecteurs à l'aide d'un questionnaire. Ce travail s'intéresse notamment à la notion de créativité en traduction, à la figure du lecteur, aux stratégies d'étrangéisation et de domestication, ainsi qu'à la traduction coloniale et postcoloniale. L'œuvre choisie est un roman de prose poétique écrit à la mémoire du jeune garçon Ojibwé Chanie Wenjack, décédé après avoir été placé de force dans un pensionnat indien au Canada.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Thesis on literary translation strategies for a postcolonial novel; the object is translation practice, not research.
It studies literary translation strategies and reader responses, not research practice.
Literary translation thesis on postcolonial strategies and reader response; not metaresearch.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".