Stripping Autonomy: Coloniality and the Production of Territory vis-a-vis Canada’s Exotic Dancer Visa Program
Bibliographic record
Abstract
This paper traces the making and unmaking of a unique former temporary visa program in Canada: the Exotic Dancer Visa Program. Using theories of territory and colonialism, I imaginatively analyze the construction of the Canadian state and migrant exotic dancer bodies as spaces reflective of the surrounding social order to recognize structurally violent discourses and ideologies. Drawing connections between Canada’s history of colonial nation-building tactics as a white settler nation, I ask that we recognize the continued legacy of colonialism within the state’s immigration policy to unsettle and discomfort our complacency within a “post-colonial” world. By conceptualizing the body as a space, this thesis reimagines territory using theories on geographic scale so that the internalized subjectivities produced by the EDVP can be understood to constitute migrant territories which are peripheral within the dominant territory of the state. Drawing from press coverage of various formative scandals, and court cases regarding legal activity within strip clubs, I interrogate the ideologies and discourses perpetuated by the media which constructed not only the EDVP in itself, but the migrant and state territories. Ultimately it is my hope that by exploring the production of colonial territories in relation to the EDVP, a space for discomfort and recognition of complacency within a world dominated by violence and discourse can be acknowledged, and inspire self-reflection and change.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.036 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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 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".