A Bridge for Change: Four Award-Winning Canadian Novels and Their Engagement with National Discourses (1981-2015)
Bibliographic record
Abstract
Since the Multiculturalism Act of 1971, Canadian literature has resounded with the voices of racially and ethnically marginalized Canadians striving to contribute their own stories and experiences in a country that prides itself on its inclusivity. It is problematic then, when novels that are a part of the Canadian canon or contribute to Canadian cultural capital in some way, offer a narrative that allows integration only to white males. In Michael Ondaatje’s In the Skin of a Lion and Vincent Lam’s Bloodletting and Miraculous Cures, the narratives suggest that whiteness leads to success. Visible minorities in Lam’s novel are forced to assimilate to find success. Ondaatje deals with immigration but explores only the issues white immigrants face. He writes an immigrant success story, troubling in its premise that the only way an immigrant can successfully integrate is through their own merit. In Obasan, Joy Kogawa critiques this “truth” as she explores the ways in which visible minorities are constantly othered. Kogawa writes of the reality of the Japanese internment during World War II in Canada, and so, she depicts the injustices the Canadian government inflicted on its own citizens. André Alexis’s Fifteen Dogs is an apologue where he clearly deals with universal issues such as the perils of human consciousness, but the novel is also about race, even though Alexis never addresses this directly. Alexis is subtle in his critique of whiteness, and so, Alexis and Kogawa both critique the reality faced by non-whites in Canada. All four novels contribute to Canadian cultural capital and thus help form Canadian identity. The issues these novels either criticize or unproblematically present all point to the still-thorny dilemma of Canadian identity; is this identity based primarily on an individual’s whiteness?
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.100 | 0.036 |
| Scholarly communication | 0.031 | 0.007 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".