The evolution of law enforcement and intelligence cooperation between Canada and the United States
Bibliographic record
Abstract
In April 2023, Canada and the United States entered into an agreement to trace guns that are intercepted at the border to enhance efforts to stop the smuggling of handguns across their shared border. This was just another example of the long history of law enforcement and intelligence cooperation between the two countries. This cooperation began before Prohibition, but it intensified during that era due to the large-scale smuggling of liquor across the border. The two countries signed several agreements to facilitate cooperation, including the Liquor Smuggling Treaty of 1924 and the Liquor Clearance Act of 1930. Since Prohibition this cooperation has continued to evolve to include both formal agreement and informal arrangements. While much of this cooperation has centered on border security and cross border crime, other areas of cooperation include intelligence sharing, coordination of investigations, and sharing of biometric information. This chapter will examine the evolution this cooperation and why it is so important to address the various local and global threats the two countries face. Case studies will be utilized to illustrate this cooperation in practice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".