U.S.-Canadian Border Security: Lessons Learned from Denmarks Experience with the Schengen Convention
Bibliographic record
Abstract
This thesis examines U.S.-Canadian border threats and defenses and compares the U.S.-Canadian situation with the European Schengen Convention (SC). The Department of Homeland Security coordinates U.S. security with representatives from law enforcement, military and civilian entities. Public Safety Canada coordinates defense in Canada. Prior to the 9/11 attack, the U.S.-Canadian relationship was similar to the SC, focusing on securing external borders while opening shared borders. Some experts, especially in the U.S., argue that border security needs to be tightened further while others contend increased U.S.-Canadian border security is unnecessary and harms commerce. In 2011, Denmark decided to increase internal border security, rejecting the SC tenets requiring common security of external border of the larger Schengen area and open internal zones. The increase represented a case study of unilateral border relations. Although never completed, the lessons of this brief experiment in increased border security are that homeland security decisions are based more on arguments of sovereignty and politics, rather than on objective determinations of threats and security. More broadly, this thesis argues that the U.S. and Canada can benefit from returning to an open border and push the threat as far away as possible.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.037 | 0.013 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".