Performing Borders: Queer and Trans Experiences at the Canadian Border
Bibliographic record
Abstract
Biometric security and screening systems have revolutionized border crossings. As bodies move across the physical space of the borderland, the border moves through them, scanning and cataloguing and scrutinizing bodies for irregularity. While such technologies have been scrutinized, they have largely been so through heteronormative and cisnormative lenses that fail to recognize the vastly different experiences of nonbinary, nonconforming, transgender, and queer border crossers. This paper examines the implications of what we argue is the individualization of the border, and the effects of biometric security screenings for people whose bodies do not conform to heteronormative and cisnormative standards. We argue that border securitization increasingly equates body differences to narratives of threat and risk, which endangers nonbinary, trans, and queer border crossers, and places their safe passage at risk.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".