Clustering Application and Evaluation of the Countries' Word Risk and Climate Risk Indices
Bibliographic record
Abstract
Societies take various initiatives to reduce the impact of natural disasters. Unfortunately, certain nations and regions are better suited than others to finding solutions to the problem, whether for political, cultural, economic, or other factors. This paper deals with the cluster analysis of 170 countries based on world risk index and climate risk index data. We use the k-means approach for clustering in sequential stages of this work. Specifically, we first carry out both the elbow method and silhouette scores to determine the number of clusters. Then clustering analysis is carried out, taking into account the World Risk Index, which includes risks of both exposure and vulnerability. Second, the Climate Risk Index is implemented into the first stage results by clustering countries after determining the number of clusters. Lastly, statistical analyses on the change of clusters for exposure, vulnerability, and climate risk are investigated and discussed in detail. Taken together, each of the risk elements like earthquake, tsunami, socioeconomic development, health care capability, etc. differs by nation. Clusters of countries with similar risks are reported. When the climate risk index is included in the evaluation, the number of clusters increases. The Climate Risk Index has been determined as a variable that cannot be ignored when countries are clustered according to their risk profiles.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".