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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2602.14835 |
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| _version_ | 1866915808585711616 |
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| author | Hadfield, Evan Konya, Andrew |
| author_facet | Hadfield, Evan Konya, Andrew |
| contents | Global survey research increasingly informs high-stakes decisions in AI governance and cross-cultural policy, yet no standardized metric quantifies how well a sample's demographic composition matches its target population. Response rates and demographic quotas -- the prevailing proxies for sample quality -- measure effort and coverage but not distributional fidelity. This paper introduces the Global Representativeness Index (GRI), a framework grounded in Total Variation Distance that scores any survey sample against population benchmarks across multiple demographic dimensions on a [0, 1] scale. Validation on seven waves of the Global Dialogues survey (N = 7,500 across 60+ countries) finds fine-grained demographic GRI scores of only 0.33--0.36 -- roughly 43% of the theoretical maximum at that sample size. Cross-validation on the World Values Survey (seven waves, N = 403,000), Afrobarometer Round 9 (N = 53,000), and Latinobarometro (N = 19,000) reveals that even large probability surveys score below 0.22 on fine-grained global demographics when country coverage is limited. The GRI connects to classical survey statistics through the design effect; both metrics are recommended as a minimum summary of sample quality, since GRI quantifies demographic distance symmetrically while effective N captures the asymmetric inferential cost of underrepresentation. The framework is released as an open-source Python library with UN and Pew Research Center population benchmarks, applicable to survey research, machine learning dataset auditing, and AI evaluation benchmarks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_14835 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Global Representativeness Index: A Total Variation Distance Framework for Measuring Demographic Fidelity in Survey Research Hadfield, Evan Konya, Andrew Methodology Computers and Society Applications 62D05 Global survey research increasingly informs high-stakes decisions in AI governance and cross-cultural policy, yet no standardized metric quantifies how well a sample's demographic composition matches its target population. Response rates and demographic quotas -- the prevailing proxies for sample quality -- measure effort and coverage but not distributional fidelity. This paper introduces the Global Representativeness Index (GRI), a framework grounded in Total Variation Distance that scores any survey sample against population benchmarks across multiple demographic dimensions on a [0, 1] scale. Validation on seven waves of the Global Dialogues survey (N = 7,500 across 60+ countries) finds fine-grained demographic GRI scores of only 0.33--0.36 -- roughly 43% of the theoretical maximum at that sample size. Cross-validation on the World Values Survey (seven waves, N = 403,000), Afrobarometer Round 9 (N = 53,000), and Latinobarometro (N = 19,000) reveals that even large probability surveys score below 0.22 on fine-grained global demographics when country coverage is limited. The GRI connects to classical survey statistics through the design effect; both metrics are recommended as a minimum summary of sample quality, since GRI quantifies demographic distance symmetrically while effective N captures the asymmetric inferential cost of underrepresentation. The framework is released as an open-source Python library with UN and Pew Research Center population benchmarks, applicable to survey research, machine learning dataset auditing, and AI evaluation benchmarks. |
| title | The Global Representativeness Index: A Total Variation Distance Framework for Measuring Demographic Fidelity in Survey Research |
| topic | Methodology Computers and Society Applications 62D05 |
| url | https://arxiv.org/abs/2602.14835 |