Out of Place: Racialized Bodies at the USA-Canada Border
Bibliographic record
Abstract
The United States of America and Canada have a strong bilateral relationship that spans trade and national security concerns made necessary by their geographic proximity to one another. However, this is not one that equally impacts both states. This article maps out how marketization and securitization as dual forces shape much of Canada’s immigration policy framework. This framework is in response to American post-9/11 national security discourses which resulted in the reification of racial discrimination at the border (Crocker et al. 2007). It will do so by grounding these arguments in a theoretical framework that critically examines neoliberalism as the context in which ‘biopolitics of citizenship’ at the border emerge which further constrains the mobility of racial others across the Canada-USA border (Sparke 2006). While Canada has always been framed as a safe harbor for freed slaves, we shall discuss how Black bodies have always been marked as out of place and other at the border as early as the eighteenth century through surveillance and biometric technologies like the Book of Negroes (Browne 2015). Special attention shall be brought to bear on policy documents, legislation, and agreements like the North American Free Trade Agreement, Canada’s Anti-Terrorism Act, and the Immigration and Refugee Protection Act. This attention will situate these discussions within a regulatory framework that continues to mark racialized migrant bodies at this border site as out of place, weakening Canada’s ability to articulate a truly emancipatory vision of multiculturalism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.046 | 0.022 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".