Breaking the Cycle: Oral Histories of Trauma & Resilience among Unhoused Queer & Trans Youth in Ontario, from the 1970s to the 2010s
Bibliographic record
Abstract
This dissertation is about queer and trans folks who experienced houselessness during their youth, between the 1970s and the 2010s in Ontario, Canada. These archival and oral history stories serve to mobilize knowledge about community building, poverty, and identity-based discrimination through a history of the present. They also reveal connections between people, organizations, policies, and historical patterns of dispossession that produced the conditions which caused or continue to perpetuate houselessness. \n\tMy methodological framework is informed by several major theorists, notably Kiberle Crenshaw’s intersectionality, Eve Tuck’s desire-based framework, and Michel Foucault’s and Andrew Gorman-Murray’s analyses of space, power, and sexual embodiment. These approaches highlight the subjective uses for public space and provide ways to understand the impact of historical themes of dispossession on living, breathing people. With a focus on “houselessness” rather than the more traditional term “homelessness,” I emphasize the material scarcity associated with not having safe or stable housing, while acknowledging that many folks found a sense of home on the streets. \n\tBeginning with an analysis of these historical patterns, Chapters 1 and 2 discuss poverty in Canada from the 19th century onward, with a gradual focus on the construction of postwar heteronormativity and its impact on youth. Chapter 3 expands on themes of identity-based displacement through an analysis of the field of youth work and the question of cultural competency within service provision for queer youth. In Chapter 4, I explore the role of physical and conceptual space, with a focus on how people come together to form community. This togetherness and mutual support forms the backdrop for an analysis of trauma in Chapters 5 and 6, which ultimately posit a theory of resilience in history. In contextualizing these historical themes of poverty and disconnection with the agency of people to redefine the meaning of the past for themselves in the present, such a theory holds space for contradiction, temporal non-linearity, and reflexive understandings of personal and collective history. Taking this more fluid and discursive approach is necessary to conducting community-based work and knowledge mobilization with marginalized folks, whose subjective experiences and interpretations can often transcend traditional disciplinary approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".