The Smart Border Accord and the Schengen Agreement: A Comparative Analysis of Security Policies
Bibliographic record
Abstract
Focusing on cooperation and coordination, we compare the security policies of Europe's Schengen Agreement and the Canada-U.S. Smart Border Accord. To do so, we argue that national security is a public good and its production should be analyzed in a strategic context. We show that efficient border policies could require that countries collaborate and that the outcomes of such a collaboration are function of four fundamental factors: i) national sovereignty issues; ii) the number of participating countries; iii) prisoner's dilemma problems and iv) the payoff structure and the level of publicness related to security measures. In light of these factors, we underline the Schengen and U.S.-Canada differences. This allows us to show that the U.S. and Canada could reach optimal global security using independent border policies and a common security perimeter would not be necessary. Nous comparons les politiques de sécurité des accords de Schengen et de la frontière intelligente Canada-États-Unis en termes de coopération et de coordination. Pour ce faire, nous considérons que la sécurité nationale est un bien public dont la production doit être analysée en termes stratégiques. Nous démontrons qu'une gestion efficiente des frontières peut nécessiter que les pays participants collaborent et que les résultats de cette collaboration sont fonction de quatre facteurs fondamentaux : i) la souveraineté nationale; ii) le nombre de pays participants; iii) les problèmes de type « dilemme du prisonnier » et iv) la structure des bénéfices nationaux et le caractère public des mesures de sécurité. À la lumière de ces facteurs, nous soulignons et analysons les différences entre les accords de Schengen et de la frontière intelligente Canada-États-Unis. Nous démontrons que les États-Unis et le Canada peuvent atteindre un niveau optimal de sécurité globale en appliquant des politiques indépendantes de gestion frontalière et qu'ainsi, la mise en place d'un périmètre commun de sécurité ne serait pas nécessaire.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".