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Passports, Mobility, and Security: How smart can the border be?

2004· article· en· W2050195600 on OpenAlexaffabout
Mark B. Salter

Bibliographic record

VenueInternational Studies Perspectives · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHomeland securityContext (archaeology)Border SecurityPolitical scienceHomelandSecurity policyInternational tradeNational securitySecurity studiesPublic administrationPolitical economyComputer securityLawSociologyPoliticsBusinessTerrorismGeographyComputer science

Abstract

fetched live from OpenAlex

After September 11, 2001, a great deal of public and policy attention has been devoted to border security, passports, and the global mobility regime. This article examines the context of the global regulation of movement of individuals and the evolution of the passport in particular. It then examines the current American border security architecture. The creation of the Homeland Security Department reflects a sea-change in the view of the border, and these new policies are evaluated in regard to three cases: the U.S./Canada border, the document/examination policies at the American borders, and the European Schengen mobility regime.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.020
Scholarly communication0.0150.017
Open science0.0010.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.023
GPT teacher head0.353
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations262
Published2004
Admission routes2
Has abstractyes

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