Visualizing the regional risk in raw material supply based on event analysis
Bibliographic record
Abstract
Achieving stable and resilient mineral supply requires a good understanding of the diverse risk factors present in various regions, which vary in their severity levels. Conventional criticality assessments typically consider risk factors such as political stability and investment attractiveness using country-level indicators. However, there are severe risk factors that have yet to be incorporated due to the lack of data and methodology to evaluate them. This study quantifies country-specific risks of new risk domains such as natural disasters, accidents, and labor strikes through a meta-analysis of historical events. The risk scores for 93 source countries are calculated based on the number of records referring to those events, which were obtained through document investigation using three different approaches to event analysis. Our analysis reveals high risk scores for resource-rich developed countries like Australia and Canada due to the high frequency of events, which suggests a distinct feature of regional risk compared to the conventional domains of supply risk evaluation. This study highlights the significant potential of event analysis to provide evidence for policy design in supply chain risk management. • Broader mineral supply risks were evaluated using historical event data. • Regional risks of natural disasters, accidents and labor strikes were quantified. • Natural disaster risk in Australia and Canada is high unlike conventional risks. • Event analysis supports evidence-based policymaking in resource strategies.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".