Materialization of racialized surveillance: Lived experiences of home imprisonment
Bibliographic record
Abstract
Racialization, surveillance, and securitization may be distinct theoretical concepts, but they are nevertheless significantly intertwined. Race, as a mode of thinking and governance, largely informs the practices of securitization, whereby surveilling racialized bodies is an immanent task of the securitization process. To demonstrate this relationship, I interviewed three of the men from the infamous Canadian “Secret Trial 5” Security Certificate cases and their family members. I investigate their lived experiences of home imprisonment, examining how their home became a key site for the operation and deployment of racialized surveillance. Their experiences illustrate how surveillance emerges as a practice of securitization, where racialized “Others” are reaffirmed as threats to and subjects of unfettered surveillance practices. As the only research endeavor to interview Canada’s security certificate detainees and their families, this article demonstrates how securitization materializes through the transformation of the home into a prison; this is achieved through the imposition of carceral practices and a penal architecture within the home and through eroding belonging and safety for the people living under this type of regime. Moreover, given that most studies focusing on the experiences of securitization are restricted to the experiences of the incarcerated individuals, these studies often exclude, and by extension, silence the voices of the families also touched by these processes. Thus, this article illuminates that, albeit, in different magnitudes, families also undergo the pains of imprisonment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".