Infrastructure, Attitude and Weather: Today’s Threats to Supply Chain Security
Bibliographic record
Abstract
The global economy can be viewed today as a myriad of border-crossing supply chain networks of production, supply, distribution and marketing systems. Given the enormous value embodied in these systems, and an environment increasingly characterized by uncertainty and vulnerability, it is not surprising that concern about supply chain security has intensified. Concern takes many forms. For example, how supply chains might be used as vehicles for criminal activity (smuggling, trafficking of narcotics and importing counterfeit goods) or acts of terrorism (radio-active materials, bombs, even nukes in containers). Technology-based threats to supply chains, such as cybercrimes, data breaches and IT failures, now appear more frequently in the literature on supply chain security. These threats could result in substantial disruption to supply chains and damage to companies and their customers.Clima But larger storms are brewing, whose menace to supply chain security is greater still – and where actions to protect supply chains move more slowly. These include the continued deterioration of transportation infrastructure, a new posture on trade which views supply chains as threats to jobs and wages, and the impact of climate change. These threats do not lie off in the distant future; they are threats of today and tomorrow.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".