empirical case study. 4.3 Scope of the Contribution This paper addresses the “Trust” and “Transformation” pillars of the World Mining Congress 2026 theme, while incorporating “Technology” through the integration of territorial data systems and early warning analytics. The scope is focused on: ● Gold-producing districts where informal and formal operations coexist. ● Governance risk assessment at the district or concession level. ● Institutional and supply chain implications for responsible mineral delivery. The framework is designed to be: ● Scalable to other gold-producing countries. ● Adaptable to different extractive commodities. ● Compatible with ESG risk management systems and territorial intelligence dashboards. This contribution does not aim to replace enforcement or formalization strategies. Instead, it complements them by introducing a preventive governance layer capable of anticipating where institutional fragility may compromise responsible mineral production. 5. Methodology and Implementation Approach 5.1 Research and Operational Design This study combines conceptual modeling with empirical territorial analytics to transform the notion of legitimacy competition into a measurable governance-risk framework for mining jurisdictions. The revised methodological design integrates three complementary layers: 1. Conceptual operationalization of illegal mining as a territorial competitor for legitimacy. 2. Multi-source empirical measurement using administrative, regulatory, socioeconomic, and territorial datasets. 3. Strategic visualization and ranking tools to identify territorial stress clusters relevant for policymakers, investors, and governance actors. The objective is not to estimate criminality directly, but to detect territorial conditions under which illegal or informal mining can accumulate economic leverage, social tolerance, territorial presence, and governance influence. 5.2 Data Sources and Integration The Territorial Legitimacy Risk Index (TLRI) integrates multiple public and administrative datasets across Peru’s regions (24 departments plus Callao). Core Data Sources 31
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