The National Strategy for Global Supply Chain Security. The Path to an Intermodal Security Program
Bibliographic record
Abstract
This article will describe how the United States (US) National Strategy to Secure the Global Supply Chain represents the best effort to pave a path forward toward a secure system that maintains the free and efficient flow of goods from point of origin to point of sale through any disruption. Two overarching goals guide the US government’s global supply chain efforts: (1) “secure efficiency” to enhance the security and efficiency of the global supply chain; and (2) “dynamic resilience” to strengthen the resilience of the global supply chain against catastrophic disruptions. The strategy is about managing risks through a layered approach that capitalizes on focused measures aimed at increasing security and resilience, and improving functionality and efficiency. Specifically, the strategy recognizes the need for the US to work in conjunction with other nations and private sector partners to: (1) implement security measures throughout the global system by deterring terrorists from exploiting it as a channel for delivering harm; (2) protect infrastructure critical to the continued operation of the system; and (3) embed resilience throughout the system. The strategy also recognizes that the US must work to improve its domestic system for moving commerce. In order to improve system efficiency and functionality, the strategy must first streamline and reform government security processes. This means the US government will work to remove unnecessary security-related obstacles from the flow of lawful commerce and continuously look for ways to improve, reform, and optimize security measures. The administration will also put new emphasis on adapting and developing new technologies that achieve greater security and efficient movement of commerce. Finally, the US government will expand, develop, and modernize supply chain and border infrastructure by working with Canada and Mexico to assess needs and develop solutions to address them.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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".