La traduction des littératures autochtones en espagnol : les cas de <i>Kuessipan</i> de Naomi Fontaine et <i>Je suis une maudite sauvagesse/Eukuan nin matshi-manitu innushkueu</i> d’An Antane Kapesh
Bibliographic record
Abstract
Le présent article propose une étude comparative de la traduction espagnole de Kuessipan , de l’écrivaine innue Naomi Fontaine, réalisée par Luisa Lucuix et publiée par Pepitas de calabaza en 2020, et de la traduction argentine de Je suis une maudite sauvagesse/Eukuan nin matshi-manitu innushkueu de l’autrice innue An Antane Kapesh, réalisée par Violeta Percia et publiée par Espacio Hudson en 2023. L’analyse des enjeux éditoriaux relatifs à ces publications à partir d’une perspective sociologique permet de souligner la relation entre les projets de traduction spécifiques et leur contexte de production. En même temps, cet article postule l’émergence d’un nouveau domaine au sein des études littéraires autochtones et des études sur la traduction des livres autochtones, en raison des problèmes spécifiques que pose la publication en espagnol de ces ouvrages dans les contextes distincts de l’Europe et de l’Amérique latine.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".