Border cracks: approaching border security from a complexity theory and systems perspective
Bibliographic record
Abstract
Presently, U.S. border security endeavors are compartmentalized, fragmented, and poorly coordinated. Moreover, international collaborations are extremely limited; success hinges on effective international cooperation. This thesis addresses U.S. border security management using complexity theory and a systems approach, incorporating both borders and all associated border security institutions simultaneously. Border security research has rarely viewed all stakeholders as a holistic unit up to this point, nor has border security been thoroughly examined using a systems approach. This research scrutinizes the current U.S. border security paradigm in an attempt to determine the systemic reasons why the system is ineffective in securing U.S. borders. Additionally, the research investigates the current level of international cooperation between the United States, Canada, and Mexico. This thesis increases awareness and will possibly create dissent among established agencies, which is the first step in instituting needed changes that will ultimately increase North American security. The thesis contends that the establishment of a tri-nationalUnited States, Canadian, and Mexicanborder security agency, in addition to legalizing drugs and reestablishing a guest worker program, will be more effective and cost-efficient in securing North American borders.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".