Border Security: DHS Progress and Challenges in Securing the U.S. Southwest and Northern Borders
Bibliographic record
Abstract
Testimony issued by the Government Accountability Office with an abstract that begins "As part of its mission, the Department of Homeland Security (DHS), through its U.S. Customs and Border Protection (CBP) component, is to secure U.S borders against threats of terrorism; the smuggling of drugs, humans, and other contraband; and illegal migration. At the end of fiscal year 2010, DHS investments in border security had grown to $11.9 billion and included more than 40,000 personnel. To secure the border, DHS coordinates with federal, state, local, tribal, and Canadian partners. This testimony addresses DHS (1) capabilities to enforce security at or near the border, (2) interagency coordination and oversight of information sharing and enforcement efforts, and (3) management of technology programs. This testimony is based on related GAO work from 2007 to the present and selected updates made in February and March 2011. For the updates, GAO obtained information on CBP performance measures and interviewed relevant officials."
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".