What question did this study set out to answer?

To provide a transparent and reproducible framework for sovereign comparative evaluations across multiple domains.

July 9, 2026Open Access

SEL-OMEGA™ v2: Sovereign Evaluation & Leadership

Key Points

To provide a transparent and reproducible framework for sovereign comparative evaluations across multiple domains.
Development of a multi-domain composite index incorporating AI readiness, legal capacity, resilience, and cybersecurity preparedness.
Statistical techniques employed include Pearson and Spearman correlations, PCA, and reliability assessments via Cronbach's Alpha.
Comprehensive dataset harmonization and validation conducted using Python within Google Colab.
The publication includes statistically validated outputs with a fully ranked country dataset.
Comprehensive reproducibility artifacts are provided, supporting independent verification of results.
Robustness and stability of ranking are assessed through confidence analysis and tier classification.

Abstract

SEL-OMEGA™ v2: Sovereign Evaluation Composite Index; Artificial Intelligence; AI Governance; Governance Indicators; National Resilience; Post-Quantum Cryptography; Cybersecurity; Multi-Criteria Decision Analysis; PCA; Entropy Weight Method; CRITIC; Ensemble Weighting; Statistical Validation; Rank Robustness; Reproducible Research; Computational Social Science; Global Comparative Assessment; Data Harmonization; Python; Google Colab. Copyright and Permissions Copyright © 2026 Bidyut Mazumdar. All Rights Reserved. This publication, including its methodologies, mathematical formulations, computational models, algorithms, analytical workflows, datasets, software architecture, documentation, figures, tables, computational outputs, metadata, and associated materials, constitutes the intellectual property of the author and is protected under applicable copyright laws. Permission is granted solely for: Academic research Educational use Scholarly review Non-commercial scientific study The following activities require explicit prior written permission from the copyright holder: Commercial use Commercial redistribution Commercial licensing Commercial consulting Commercial software integration Software-as-a-Service (SaaS) deployment Commercial AI model training using this work Derivative commercial products Technology transfer Commercial intellectual property licensing Monetization of any component of this publication All intellectual property rights, ownership, and associated rights remain exclusively with the author. Unauthorized commercial use, redistribution, derivative commercial development, or exploitation of any part of this publication is prohibited without prior written authorization from the copyright holder.

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