A Matter of Trust: Expanding the Preclearance of Commerce between Canada and the United States
Bibliographic record
Abstract
The thickening of the Canada-US border in response to post 9/11 security challenges has created new obstacles to cross-border trade and investment. However, preclearance of people and goods before they arrive at the physical border offers one of the best ways to address crossborder obstacles while ensuring public safety. Preclearance has a track record of success where it has been applied. It has proven to be a cost-effective way to provide air travelers with an economical way to clear customs and immigration before entering the United States from Canada. EU members are satisfied that they have succeeded in guarding their security, while promoting an integrated single market, by relying on a perimeter approach to preclearance. Canada and the United States currently rely on preclearance for trusted traveler and shipper programs. Building on these successful models, Canada and the United States should proceed to designing and implementing ambitious programs that use preclearance to cost-effectively ensure a secure and economical border crossing, for most travelers and shippers.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.035 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.032 | 0.033 |
| 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".