Bibliographic record
Abstract
U.S. soldiers continue to fight the war on terror overseas, but what about the homeland? Our borders, 5,525 miles with Canada and 1,989 miles with Mexico, are left ineffectively guarded for millions of people to cross into the U.S. illegally. In fact, more than five hundred million people cross the U.S. borders each year and roughly three hundred and thirty million of them are non-citizens. Even after the horrific events of September 11th 2001, little has been done to secure the nation's borders. Last spring the Departments of Treasury and Justice requested support from the Department of Defense to support the Immigration and Naturalization Service (INS) and U.S. Customs Service (USCS) in securing the borders. After reviewing the requests, a memorandum of agreement was signed. The Department of Defense (DoD) mobilized sixteen hundred personnel from the Army National Guard for active duty to support federal law enforcement agencies along the borders. In May 2002 the U.S. Army Forces Command and the U.S. Joint Forces Command completed an assessment of the mission. The resulting report recommended that approximately three hundred soldiers working in remote areas along the border could carry weapons. In addition to these efforts of securing our borders by DoD and Departments of Treasury and Justice, congress voted in favor of legislation to close border security gaps. This legislation authorized congress to provide more funding to the INS to hire more border agents, upgrade salaries and buy much needed equipment. At the end of fiscal year 2002, this temporary mission came to an end. The men and women of the Army National Guard are no longer supporting U.S. borders. While there is discussion in congress about continuing this mission, at this point, no one is certain if it will.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".