11: “Have I Got Stories—” and “Coyote Was There”: Thomas King’s Use of Trickster Figures and the Transformation of Traditional Materials
Bibliographic record
Abstract
T homas K ing's engagement with trickster figures, Coyote in particular, has long roots. In his 1986 dissertation “Inventing the Indian: White Images, Native Oral Literature, and Contemporary Native Writers” King wrote: “If there is a need to understand a culture, and one can only hear a single story that the culture tells about itself, that story should probably be a creation story” (King 1986, 69), and of course Coyote was there at the beginning of things. In his anthology of contemporary Canadian Native literature in English, All My Relations , he depicts the trickster as “an important figure for Native writers for it allows us to create a particular kind of world in which the Judeo-Christian concern with good and evil and order and disorder is replaced with the more Native concern for balance and harmony” (King 1990b, xiii). In his collection of short stories, One Good Story, That One (1993) Coyote appears in the title story in a Native version of the biblical story of the Garden; multiple blue Coyotes transport rock-hard Indians to a space ship in “How Corporal Colin Sterling Saved Blossom, Alberta”; Coyote disastrously “fixes” the world in “The One About Coyote Going West”; and Coyote tries unsuccessfully to play ball with Columbus in “A Coyote Columbus Story.” Coyote is also central to King's major work Green Grass, Running Water , which opens and closes with Coyote's presence when the world began: “So. In the beginning, there was nothing. Just the water. Coyote was there …” (1993a, 1).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".