âOne Small Wayâ: Racism, Redress, and Reconciliation in Canadian Women's Fiction,1980-2000
Bibliographic record
Abstract
Canada’s Multiculturalism Act insists that Canada embraces its ethnic and racial diversity. At the same time, the broader discourse of multiculturalism tends to figure Canada as a tolerant but essentially white nation that accommodates minority cultures. In an attempt to expand established arguments about the ways in which the ideology and practice of official multiculturalism elides our history of racism and violence and perpetuates racist myths and stereotypes, this dissertation examines the depiction of a civil, multicultural nation in women’s fiction produced during Canada’s multicultural period of the 1980s and 1990s. With an eye to understanding the particular challenges that women who have been subject to racially-motivated violence and discrimination face in relating their experience, it considers the innovative ways in which fiction by Joy Kogawa, Anne Michaels, Eden Robinson, Bharati Mukherjee, Anita Rau Badami, and Catherine Bush grapples with the effects of systemic racism. While these writers explore the gendered trauma of women who have been subjected to racism, they do not depict their protagonists primarily as victims. Instead, they show these women forging innovative strategies to overcome trauma and victimization, and their silencing and debilitating effects.\n\tIn exploring the merits of those strategies to understand how they might help us to grapple with the legacy of systemic racism and of the multicultural discourse that has sometimes masked racism in this country, I argue that literature can foster empathy in its readers, while demanding that we acknowledge our complicity with a social and political system that has frequently been racist, exclusionary, and even violent. Throughout the dissertation, I argue that the strategies for overcoming the traumatic effects of racism employed by these authors not only challenge conceptions of Canada as a civil, nonracist society, but also offer ways of extending our understanding of Canadian civility and diversity. In doing so, I suggest that Canadian literature can offer its readers the opportunity to accept responsibility for the abuses of our collective past and conceive of a more accepting, equal society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".