"The One About Coyote Going West." A Translation of a Short Story by Thomas King
Bibliographic record
Abstract
The main task of this assignment was the translation of a short story by the Canadian writer Thomas King. The story is called “The One About Coyote Going West” and is found in King’s collection of short stories One Good Story, that One. Because the main character, Coyote, in King’s short story is a culture-specific figure of Native American myth and not commonly known in Icelandic culture, some context for the character is needed. \nIn order for the reader to fully understand and enjoy the translation of King’s short story, some background knowledge is vital. A brief introduction to the life and works of Thomas King is the first chapter of the essay. Getting to know the author and his works is important for a richer understanding of the translation itself, because King tends to use Coyote as a spokesperson for Aboriginal rights and as the embodiment of the preservation of Native culture. What follows is a brief introduction of Native American myth, with an emphasis on the importance of oral storytelling and the figure of Coyote within this mythology. This is also an important chapter because King strives to capture the qualities of Native oral literature. In order to accommodate a closer understanding of Coyote as a trickster figure there is a comparison of him/her and Loki in Germanic mythology, Loki being a commonly known figure within Icelandic culture. This contextualization of King’s story concludes with a brief commentary on problems encountered during my translation of it into Icelandic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".