"For better or worse, I am Canadian." Demand for Ethnic Recognition in Green Grass, Running Water by Thomas King and Obasan by Joy Kogawa
Bibliographic record
Abstract
This essay examines the discrimination that natives and Japanese Canadians have suffered at the hands of the Canadian government through the ages and how it is reflected in the novels Green Grass, Running Water by Thomas King and Obasan by Joy Kogawa. Although initially colonized by France and England, Canada eventually came under English domination. A nation of diverse identities due to emphasis on immigration since late 19th century, Canada adopted an official multicultural policy in order to accommodate the cultural diversity of the nation. \nIn this essay I consider how two Canadian minority writers, King and Kogawa, reject the idea of “universal” or traditional writing and draw instead upon their own cultural tradition regarding literature. In comparing similarities and differences in the novels, I demonstrate in what way these writers present their criticism of the Canadian government's actions, especially regarding the appropriation of the native Canadian land and the incarceration of Japanese Canadians at the time of WWII. King and Kogawa present a clear difference in values that is unique for each novel. Kogawa's narrative suggests that Japanese long for assimilation into dominant society as individuals, but King's that natives wish to keep their own culture and to be acknowledged as a separate nation. However, I find that despite the basic difference between the novels, the demand for ethnic recognition is the same in King's Green Grass, Running Water and Kogawa's Obasan.
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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 | 0.001 |
| Science and technology studies | 0.029 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".