Does “Smarter” Lead to Safer? An Assessment of the US Border Accords with Canada and Mexico
Bibliographic record
Abstract
Abstract The terrorist attacks of September 11 and their immediate aftermath along the US‐Canadian and US‐Mexican borders focused attention on border management strategies in ways previously unimaginable. Suddenly confronted by the fact that existing systems and processes were not particularly effective either at protecting security or facilitating legitimate traffic, the United States, in conjunction with the Canadian and Mexican Governments, demonstrated an uncharacteristic willingness to reconceptualize its approach to physical borders. While initiating a series of internal policy adjustments to secure themselves against terrorist threats, the US, Canadian, and Mexican Governments also signed two bilateral agreements — the 12 December 2001 United States‐Canada Smart Border Declaration and the 22 March 2002 United States‐Mexico Border Partnership Agreement. These agreements represent an important development in the US's relationship with each of its North American neighbours, acknowledging not only the deep economic, social, and cultural ties, but also the new reality that the United States cannot attain the additional security it desires through unilateral actions alone. Thus, while September 11 forced a reassessment of vulnerabilities, it simultaneously provided the United States an opportunity to work more systematically with its contiguous neighbours for security benefits, a realization likely to flow into other areas where the benefits of cooperation eclipse those of unilateralism. This paper analyses the first year of the two border accords, tracking their implementation and evaluating their successes and failures. Most importantly, the paper outlines outstanding challenges, highlights steps that the governments should take to achieve additional border security and efficiency, and draws conclusions regarding factors likely to make their efforts more, or less, successful.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".