Clowning Canada: Performing the Structured Innocence of Settler Colonial Domesticity
Bibliographic record
Abstract
This dissertation is a research-creation project that involves both creative and written outputs.\n\nThe creative project is an original short film, Land Hunger—a dark, feminist, clown satire of settler colonial nation-state building that explores how gendered histories of domestication were foundational for colonization in Canada and are still viscerally haunting the present. The written component incorporates critical theory, personal reflection, creative writing, and historical research to explore the question: What performance praxes are needed right now for settler artists to create work that pushes against hegemonic yet invisibilized structures of Canadian nationhood? While my research looks at the potential for settler performance practices to unsettle our cultural stories of Canadian-ness, my inspirations and theoretical anchors come from the fields of Indigenous resurgence and decolonization, which centre relationality to the land within research. My theoretical framework grapples with the question of settler artist accountability and responsibility within performance practice, stemming from the argument that culture is integral to the creation and maintenance of power structures (Said, 1994). I follow a genealogical process in the Foucauldian sense, which entails sifting through hauntings between the past and present to find not the origin of nation but rather its palimpsestic and discursive formation on the land and the gendered body.\n\nThis project originated in the messy, personal, and creative questioning of my relationship to the land I am on, to the nation-state of Canada, to the histories that brought me here, and to the discourses of power that weave through them. I argue that theories of settler colonialism, gender, and whiteness are not just the subject of my research, coincidentally resulting in a creative output; rather, these theories drive the foundational way that I come to understand my relationship to the land and to my performance practice. I attempt to chart an approach to performance practice that makes this central, arguing that settler theatre artists are structurally implicated in the ongoing reification of the colonial project of Canada. Therefore, as storytellers, we can work to make visible and imagine alternatives to white settler structures of nation and subjecthood that are often normalized in settler culture as invisible and unchangeable.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.019 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".