<![CDATA[Governance indicators are widely used in global policy and development to translate complex institutional conditions into comparable numerical metrics that guide decision-making, investment, and reform strategies. Despite their importance, composite indicators such as the Worldwide Governance Indicators (WGI) face persistent challenges related to measurement validity, source heterogeneity, and structural bias in data aggregation processes. This study investigates the measurement architecture of the WGI by analyzing 31 source units derived from metadata covering governance-related datasets. The WGI integrates multiple data sources, including surveys, expert assessments, and institutional records, to measure dimensions such as the rule of law, government effectiveness, and corruption control, covering more than 200 countries worldwide (World Bank, 2024). To address this gap, this study applies a System Shift framework that conceptualizes governance measurement as the interaction between structural pressure (SC, DL, AC, CP) and adaptive capacity (POS, STR, FB). The methodology includes correlation analysis, regression models, Random Forest, and cluster analysis to evaluate measurement risk, source quality, and representativeness. The findings show that chokepoint severity strongly increases measurement risk, while adaptive variables improve source quality and reliability. The System Shift Risk Score outperforms traditional source-type classification in predicting measurement outcomes. In conclusion, governance measurement quality is determined not only by indicator aggregation but also by the structural architecture of data sources.]]>
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