Abstract
<title>Abstract</title> <p>The global push toward "strong dual necessity" frameworks in multidimensional policy indexing requires aggregating threshold-anchored scores via Constant Elasticity of Substitution (CES) functions to prevent severe deficits from being masked by excellence. However, absolute policy thresholds inherently generate negative deprivation scores, causing standard CES functions to encounter computational domain collapse. Naive computational fixes inadvertently reward statistical ignorance. This paper introduces a unified methodological architecture centered on a critical methodological hazard we term the Epistemic Shielding Trap: deriving computational buffers from empirical variance or measurement error geometrically shields poorly measured regions from Leontief bottlenecks. We resolve this via a Global Normative-Anchor Translated CES and a mathematically rigorous certainty-equivalent pre-screening mechanism. This preserves non-compensatory geometry while strictly penalizing statistical ignorance via an input-space additive shift. A stylized numerical trace, Monte Carlo validation, and an empirical proof of concept using subnational Demographic and Health Surveys (DHS) data demonstrate the framework's capacity to correct ranking distortions. This framework is explicitly designed as a longitudinal tracking and internal diagnostic audit tool to measure the 'data quality dividend' over time, and policymakers are strictly warned against utilizing the penalized index for direct cross-sectional funding allocation due to the inherent 'double punishment' of data-poor regions.</p>