The Effects of Biometric Border Systems on Trans Travelers
Bibliographic record
Abstract
The Canada-United States border is the longest land border in the world, stretching more than five thousand miles. The narrative of the Canada-United States border as the “longest undefended border in the world” was reliant on racist images of Canada as a white state. Following the attack on September 11, 2001, this narrative was disrupted and the border became the site of a massive security reconstruction project. Not in the physical sense, but in the scale of funds allocated and technology implemented to control and monitor the flow of movement across the border. Canada’s 2001 budget allocated $1.2 billion towards border security initiatives, and the United States’ 2003 national security budget saw a 1,000 percent increase from the pre-9/11 amount. Additionally, the two countries adopted biometric data into their identification documents and began a system of information sharing to process the information of travelers at the border. Biometric surveillance and the linking of identity to documents became a central feature of the securitized border. It is important to recognize that this project did not affect everyone equally. The adoption of biometric technologies at the border tends to reinforce existing hierarchies. Biometric technologies rely on outdated notions of racial and gender differences to link identity to an individual and manage risk at the border. This process of reinforcing outdated notions of gender and identity will be explored by examining how trans people are adversely affected by the deployment of biometric technologies at the Canada-United States border following the terrorist attack on September 11th.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".