Western Monkeys, Eastern Coyotes: Trickster Strategies in Resistance
Bibliographic record
Abstract
This MA thesis aims to explore how contemporary Native writers and diasporic Chinese American writers employ humor in their works through the archetypical figure of the Trickster, to articulate their resistance to racism and cultural stereotyping. By examining a selection of Coyote stories by Thomas King, a novel by Gerald Vizenor and a novel by Maxine Hong Kingston, and the ways they adapt their mythical Tricksters reinscribing them in a contemporary setting, and the way these writers juggle with words and meanings, I hope to further reveal their intentions to resist and contest hegemonic dominance. This thesis is divided into three main sections. First, the concept of Trickster as a mythological and universal archetype; second, the different deployments of this figure in contemporary Native literature, and third how it is treated in Chinese American literatures. My thesis is that literary tricksters articulate the anxieties Native peoples and Chinese migrant communities experience in the United States and Canada, calling for them to rewrite their history and reject the assigned (mis)representation through humor.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".