SEL-OMEGA™ v2: Sovereign Evaluation & Leadership

SEL-OMEGA™ v2: Sovereign Evaluation & Leadership – Canonical Publication Release (Version 2.1.0) Description SEL-OMEGA™ v2 (Sovereign Evaluation & Leadership) is a reproducible computational research framework for comparative sovereign assessment based on standardized multi-domain indicators and transparent composite index methodology. The framework integrates four complementary dimensions of national capability to support rigorous comparative analysis: Artificial Intelligence Readiness (AI_INDEX) Legal and Governance Capacity (LEGAL_WGI_SCORE) National Resilience (RES_INDEX) Post-Quantum Cybersecurity Preparedness (PQC) The objective of this release is to provide a fully documented, transparent, and reproducible computational framework for sovereign-level comparative evaluation suitable for academic research, methodological validation, evidence-based policy analysis, and computational social science. This publication represents the complete computational release, including datasets, statistical validation, indicator weighting, ranking methodology, reproducibility artifacts, documentation, publication-quality figures, computational metadata, and audit records. Research Highlights This publication implements a deterministic computational workflow emphasizing: Transparent multi-indicator composite index construction Fully reproducible computational pipeline Canonical dataset harmonization Comprehensive statistical validation Multiple objective weighting methodologies Robustness and stability assessment Publication-quality outputs Complete computational reproducibility Independent verification through integrity records End-to-end documentation and auditability Methodological Framework The computational workflow consists of a complete publication-grade pipeline including: Dataset inventory and acquisition Canonical dataset selection Country harmonization Data quality assessment Missing value verification Descriptive statistical analysis Pearson correlation analysis Spearman rank correlation Kendall Tau correlation Multicollinearity (VIF) assessment Reliability assessment using Cronbach's Alpha Outlier detection Principal Component Analysis (PCA) Entropy Weight Method (EWM) CRITIC objective weighting Ensemble (Meta) weighting Composite score computation Deterministic global ranking Tier classification Rank robustness analysis Rank confidence estimation Publication table generation Publication-quality figure generation Documentation audit Reproducibility package generation Manifest creation SHA-256 integrity verification Execution log generation Final publication assembly Release audit and validation All computational analyses were executed using Python within Google Colab, ensuring methodological transparency and computational reproducibility. Statistical Validation The publication includes comprehensive statistical validation consisting of: Descriptive Statistics Pearson Correlation Matrix Spearman Rank Correlation Kendall Tau Correlation Variance Inflation Factor (VIF) Cronbach's Alpha Reliability Assessment Outlier Analysis PCA Explained Variance PCA-Derived Indicator Weights Entropy Weight Method CRITIC Weighting Ensemble Weight Construction Rank Robustness Assessment Rank Confidence Analysis Tier Distribution Analysis Included Research Assets The publication package contains: Final Publication Ranking Complete Country Ranking Dataset Tier Classification PCA Weights Entropy Weights CRITIC Weights Ensemble Weights Pearson Correlation Matrix Spearman Correlation Matrix Kendall Tau Matrix Cronbach's Alpha Report Outlier Report Statistical Audit Report Rank Robustness Analysis Rank Confidence Report Publication Tables Publication Figures Manifest File SHA-256 Checksums Execution Log Environment Specification Python Requirements Documentation Citation Metadata License Documentation Methodology Documentation Publication Metadata Release Audit Reports Reproducibility This release has been prepared to support transparent and reproducible computational research. The publication includes: Complete computational outputs Publication-ready datasets Computational metadata Execution logs Environment configuration Software dependency specifications Manifest records SHA-256 checksum verification Documentation files Publication figures Audit reports These materials enable independent verification of the computational workflow and published results. Citation Please cite this specific publication using the DOI assigned to this release. Concept DOI (All Versions) 10.5281/zenodo.18695239 All versions of this work may be cited using the Concept DOI above. Author Bidyut Mazumdar Independent Research Scholar Founder, FAIR+D Canon™ (India, 2025) ORCID https://orcid.org/0009-0007-5615-3558 Keywords Sovereign Index; 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.