Judging Borders: Expanding the Canada-United States Border Through Legal Decision Making
Bibliographic record
Abstract
This chapter considers the role jurists have in expanding the space(s) of border work, by specifically investigating the way a challenge to the Safe Third Country Agreement was taken up within Canadian courts. Using Canada Council of Refugees v. Canada as a case study, this chapter re-centers our discussion of borders as legal spaces by examining how lawyers and judges frame the legal space of the border. The chapter proceeds by first explaining Canada Council v. Canada and its trajectory through the courts. By showing the way legal standing is made and spaces of bordering are conceptualized, lawyers and judges alike open up new territory to the work of bordering. By focusing on the law, which privileges establishing clarity and setting up relational positions, this chapter investigates how legal spaces are made. Drawing upon research into bracketing and performativity, this research suggests that understanding the way judges produce legal meaning at the border offers valuable insights toward the expansion and creation of new borders in North America.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.045 | 0.041 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".