United States Land Border Security Policy: The National Security Implications of 9/11 on the Nation of Immigrants and Free Trade in North America
Bibliographic record
Abstract
The 9/11 terrorist attacks spawned heated debates about border security roles in preventing terrorism. The United States is generally known as a "nation of immigrants," welcoming those seeking economic and religious freedom. This thesis explores the effects of three policy options (increased manpower/financial resources for border inspection agencies, technology, and private sector-government cooperation) on the prevention of terrorism within U.S. borders. It also explores the effects of those policy options on trade flows and the movement of legitimate people across international borders. Scope is limited to land border security policy from 1990-2003. Three case studies are included: (1) the Border Patrol's "prevention through deterrence" strategy, which began in 1994 and benefited from a monumental increase in manpower/financial resources to the INS; (2) an analysis of which border technology options are the most secure and inexpensive means of preventing illegal immigration, stopping the introduction of contraband into the United States, and maintaining legitimate flows of commerce/people that have increased since the passage of NAFTA; and (3) an analysis of why private sector-governmental partnerships that both increase transportation security while lowering border wait times developed on the U.S.-Canadian border but not on the U.S.-Mexican border. Implications are drawn for U.S. policy-makers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".