(Non)Translation as Resistance in Tomson Highway's Kiss of the Fur Queen
Bibliographic record
Abstract
After a brief explanation regarding the author’s settle-scholar status in regard of interpreting Indigenous texts, Tomson Highway’s novel Kiss of the Fur Queen is examined as a ‘first translation’ in which untranslatability plays the main role. The term ‘first translation’ will be defined, and a deliberately refined definition of a hybrid text will be reviewed through the lens of several Indigenous scholars. Then, following a brief description of Highway’s novel, the paper will envisage its translatory nature from the point of view of three narrative strategies: 1) The insertion of Cree lexical elements within the text. Here, in a codified manner, Highway forces the reader to grasp the importance his mother tongue has in understanding the novel’s complexities. 2) This is followed by a section on the use of Cree mythology within the narrative. Gerald Vizenor’s use of Bakhtin becomes a useful tool in accessing the idea of two consciousnesses through the intertwining of the fantastic and mythology. 3) And finally, the linguistic challenge of cultural contact within the story itself is examined. From the foreignness of English, quite literally attached to the sound of the language, to the inability of expressing the reality of abuse endured in residential school in Cree, the protagonists push up against irreconcilable cultural/linguistic worlds. Put together, these three different narrative strategies come together to form a langue culture, to use Henri Meschonnic’s term.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".