Dispensing Irregular Justice: State Sponsored Abductions, Prisoner Surrenders, and Extralegal Renditions Along the Canada–United States Border
Bibliographic record
Abstract
In 1899, Levi Edwin Dudley, the American consul at Vancouver, complained about the ways that Canadian and American police officers enacted justice along their shared border. During one of Dudley's investigations into alleged abuses, he spoke with a Canadian officer about the ways that local agents on both sides of the border approached their jobs. The officer, speaking under conditions of anonymity, noted that “on the border here we must do things in an irregular way in order to preserve the peace.” The ability of criminals to move back and forth across the line forced American and Canadian officers to “‘stand in’ with each other, [or] we should have the country filled with desperadoes.” American officers transferred criminals over to Canadian agents without proper clearance and Canadian officers later returned the favor. This system of irregular justice utilized informal prisoner exchanges built on local understandings, professional courtesy, and mutual concern to circumvent the slow, uncertain, and expensive extradition process. For Dudley, this kind of behavior threatened the liberty of citizens in both countries. For the officers tasked with policing a region of bisecting jurisdictions, it was a necessary evil.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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 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".