Diffuse Connections: Making Sense of Smell in Canadian Diasporic Women's Writing
Bibliographic record
Abstract
This dissertation explores the crucial, yet often unacknowledged, role smell plays in Canadian diasporic women’s writing. While some critics discuss scent in their work on taste, memory, and diasporic nostalgia, I argue for considering scent in its specificity and suggest that smell shapes diasporic subjectivities differently than taste. Complicating frameworks that focus primarily on notions of memory, homeland, and nostalgia, I consider how diasporic subjectivities are shaped by a range of feelings connected to experiences in past homelands and present places of habitation, including racialized and gendered forms of olfactory discrimination in the ostensibly tolerant nation of Canada. Appropriating the concept of diffusion from scientific theories of smell, I re-conceptualize diffusion as a model of movement and mixing that complicates narratives of linear diasporic migrations from a single point of origin. I use diffusion to theorize “diffuse connections,” a framework that emphasizes the blending of diasporic experiences across time and space and the intimate intersubjective encounters that emerge through scent. Each chapter explores novels by Canadian diasporic women writers (Shani Mootoo, Hiromi Goto, and Larissa Lai) that represent diasporic subjectivities in terms of diffuse connections.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".