Bibliographic record
Abstract
This text provides an integrated view of post-9/11 security concerns over the United States's shared border with Mexico and Canada in regards to terrorism, unauthorized migration, drug and arms smuggling, and other illegal trade. The challenges facing U.S. Customs and Border Patrol are daunting. There are 19,841 miles of American land and water boundaries to protect, and 95,000 miles of shoreline and defined air space subject to homeland security surveillance. Additionally, the booming drug trade across the U.S.-Mexico border, combined with the ever-increasing number of migrants wanting to reach our land of opportunity, has resulted in a grim death toll: more than 5,000 known migrant deaths have occurred along the U.S.-Mexico border during 1995–2008, and in 2009, an estimated 9,635 Mexicans were killed in drug-related violence, with 2,573 people killed in Ciudad Juarez alone. U.S. Border Security focuses on the contrast between border security before and after the 9/11 terrorist attacks. This text also examines the controversial topics of illegal immigration, counterterrorism, drug and weapons trafficking, human smuggling, the impact of border security on the movement of people and goods, and the effect of the war on terrorism on civil and human rights.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.169 | 0.101 |
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".