Managing the nation's borders during the global war against terrorists
Bibliographic record
Abstract
Terrorism: Documents of International and Local Control is a hardbound series that provides primary-source documents on the worldwide counter-terrorism effort. Chief among the documents collected are transcripts of Congressional testimony, reports by such federal government bodies as the Congressional Research Service and the Government Accountability Office, and case law covering issues related to terrorism. Most volumes carry a single theme, and inside each volume the documents appear within topic-based categories. The series also includes a subject index and other indices that guide the user through this complex area of the law. Managing the Nation's Borders During the Global War Against Terrorists presents three viewpoints on the problem of securing U.S. borders: the U.S. government's self-assessment, the often critical judgment of independent agencies like the Government Accountability Office (GAO) and the Congressional Research Service (CRS), and General Editor Douglas C. Lovelace's own critique both of the governmental pronouncements and of those GAO/CRS reports. By presenting both the text of border-related regulations and these three perspectives on those regulations' effectiveness, Lovelace provides researchers with a one-volume, comprehensive exposition of the topical issue of border security. Even more importantly, the documents and commentary in this volume will provoke policymakers and other government staff into thinking differently and creatively about the challenge of securing borders that extend for thousands of miles over often harsh terrain. For example, Lovelace and some of the included authors challenge the notion that physical barriers alone will impede the entry of terrorists. Similarly, Lovelace here encourages his readers to envision borders not just as a means to regulate crime but also as a vehicle for international cooperation between, in this case, the U.S., Mexico, and Canada. This volume is essential for any researcher seeking a current, tough-minded analysis of U.S. border security.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".