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Record W7096853543

Technology for Border Protection: Homeland Security Funding and Priorities.” Homeland Security Journal

2003· article· en· W7096853543 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicBorder Security and International Relations
Canadian institutionsnot available
Fundersnot available
KeywordsHomeland securityBorder SecurityHomelandFree flowNational securitySecurity studies
DOInot available

Abstract

fetched live from OpenAlex

By any measure, protecting the United States ’ borders and ports pose mammoth challenges due to the sheer size of the task:1, 2 · 500 million people crossing the borders each year · 5,525 miles of Canadian border · 1,989 miles of Mexican border · 95,000 miles of shoreline · 350 commercial ports of entry · 21,000 containers entering U.S. ports each day3 At the same time, the need for border protection must be balanced against the demand for the free flow of c ommerce. In 2000, trade with Canada and Mexico alone totaled $653 billion.4 Recognizing the importance of international trade to the U.S. economy, Congress tasked the Homeland Security Department not only with protecting the borders, but also “ensuring the speedy, orderly, and efficient flow of lawful traffic and commerce.”5 How can the Homeland Security Department simultaneously protect the borders and preserve the flow of free trade, all while not busting the federal budget? Technology has

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.330
Teacher spread0.306 · 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