Technology for Border Protection: Homeland Security Funding and Priorities.” Homeland Security Journal
Bibliographic record
Abstract
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".