[
  {
    "code": "AI_LIT_001",
    "name": "OECD PIAAC Adaptive Problem Solving",
    "pillar": "AI_Literacy",
    "subcomponent": "AI literacy & skills",
    "description": "Score assessing individuals' ability to solve problems in technology-rich environments",
    "unit": "points",
    "direction": "higher",
    "validity": "High",
    "source": "PIAAC_OECD",
    "role": "Retained",
    "normalization": "z-score"
  },
  {
    "code": "AI_LIT_002",
    "name": "PISA Digital Reading Literacy",
    "pillar": "AI_Literacy",
    "subcomponent": "AI literacy & skills",
    "description": "Students' ability to access understand and reflect on digital texts",
    "unit": "points",
    "direction": "higher",
    "validity": "High",
    "source": "PISA_OECD",
    "role": "Retained",
    "normalization": "z-score"
  },
  {
    "code": "AI_LIT_003",
    "name": "LinkedIn Global Skills Gap Index",
    "pillar": "AI_Literacy",
    "subcomponent": "AI literacy & skills",
    "description": "Composite measure of AI/ML skill demand vs supply in labor market",
    "unit": "index",
    "direction": "higher",
    "validity": "Medium",
    "source": "LINKEDIN_LinkedIn",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "AI_LIT_004",
    "name": "Tertiary STEM Enrollment",
    "pillar": "AI_Literacy",
    "subcomponent": "AI literacy & skills",
    "description": "Percentage of university students in STEM fields",
    "unit": "%",
    "direction": "higher",
    "validity": "Medium",
    "source": "UNESCO_STEM",
    "role": "Retained",
    "normalization": "rank"
  },
  {
    "code": "AI_LIT_005",
    "name": "ITU Digital Skills Index",
    "pillar": "AI_Literacy",
    "subcomponent": "AI literacy & skills",
    "description": "Composite of digital literacy adoption across population",
    "unit": "index",
    "direction": "higher",
    "validity": "Low",
    "source": "ITU_SKILLS",
    "role": "Retained",
    "normalization": "rank"
  },
  {
    "code": "CAL_TRUST_001",
    "name": "KPMG-Melbourne AI Trust Study",
    "pillar": "Trust_Attitudes",
    "subcomponent": "Calibrated trust / verification behavior",
    "description": "Global survey measuring trust vs concern balance in AI",
    "unit": "index",
    "direction": "balanced",
    "validity": "High",
    "source": "KPMG_AI_Trust",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "CAL_TRUST_002",
    "name": "Ipsos AI Monitor",
    "pillar": "Trust_Attitudes",
    "subcomponent": "Calibrated trust / verification behavior",
    "description": "Multi-country survey on AI attitudes and behaviors",
    "unit": "index",
    "direction": "balanced",
    "validity": "High",
    "source": "IPSOS_AI_Monitor",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "CAL_TRUST_003",
    "name": "Pew Research AI Attitudes",
    "pillar": "Trust_Attitudes",
    "subcomponent": "Calibrated trust / verification behavior",
    "description": "U.S. and global attitudes toward AI automation",
    "unit": "index",
    "direction": "balanced",
    "validity": "Medium",
    "source": "PEW_AI_Attitudes",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "CAL_TRUST_004",
    "name": "Edelman Trust Barometer AI",
    "pillar": "Trust_Attitudes",
    "subcomponent": "Calibrated trust / verification behavior",
    "description": "Trust in AI institutions and companies",
    "unit": "index",
    "direction": "balanced",
    "validity": "Medium",
    "source": "EDELMAN_AI_Trust",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "CAL_TRUST_005",
    "name": "Lloyd's Foundation World Risk Poll",
    "pillar": "Trust_Attitudes",
    "subcomponent": "Calibrated trust / verification behavior",
    "description": "Public attitudes toward technological risk",
    "unit": "index",
    "direction": "balanced",
    "validity": "Low",
    "source": "LLOYDS_2022",
    "role": "Rejected",
    "normalization": "min-max"
  },
  {
    "code": "META_COG_001",
    "name": "PISA Reading Fact vs Opinion",
    "pillar": "Critical_Discernment",
    "subcomponent": "Metacognition / independent judgment",
    "description": "Ability to distinguish factual from opinion statements",
    "unit": "points",
    "direction": "higher",
    "validity": "Medium",
    "source": "PISA_FACTOPIN_OECD",
    "role": "Retained",
    "normalization": "z-score"
  },
  {
    "code": "META_COG_002",
    "name": "European Media Literacy Index",
    "pillar": "Critical_Discernment",
    "subcomponent": "Metacognition / independent judgment",
    "description": "Composite media and information literacy for Europe",
    "unit": "index",
    "direction": "higher",
    "validity": "Medium",
    "source": "EMLI_CouncilEurope",
    "role": "Retained",
    "normalization": "z-score"
  },
  {
    "code": "META_COG_003",
    "name": "Reuters Institute Digital News Report",
    "pillar": "Critical_Discernment",
    "subcomponent": "Metacognition / independent judgment",
    "description": "Misinformation concern and news verification indicators",
    "unit": "points",
    "direction": "higher",
    "validity": "Low",
    "source": "REUTERS_2023",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "META_COG_004",
    "name": "MediaWise Digital Literacy",
    "pillar": "Critical_Discernment",
    "subcomponent": "Metacognition / independent judgment",
    "description": "Digital citizenship and critical thinking assessment",
    "unit": "points",
    "direction": "higher",
    "validity": "Low",
    "source": "MEDIAWISE_2022",
    "role": "Rejected",
    "normalization": "min-max"
  },
  {
    "code": "DEC_AGY_001",
    "name": "Worldwide Governance Indicators",
    "pillar": "Institutional_Governance",
    "subcomponent": "Decision agency (institutional safeguards)",
    "description": "Rule of law and voice & accountability components",
    "unit": "index",
    "direction": "higher",
    "validity": "High",
    "source": "WGI_WorldBank",
    "role": "Retained",
    "normalization": "z-score"
  },
  {
    "code": "DEC_AGY_002",
    "name": "V-Dem Democracy Indices",
    "pillar": "Institutional_Governance",
    "subcomponent": "Decision agency (institutional safeguards)",
    "description": "Liberal democracy and electoral democracy indices",
    "unit": "index",
    "direction": "higher",
    "validity": "High",
    "source": "VDEM_Vdem",
    "role": "Retained",
    "normalization": "z-score"
  },
  {
    "code": "DEC_AGY_003",
    "name": "Freedom House Freedom in the World",
    "pillar": "Institutional_Governance",
    "subcomponent": "Decision agency (institutional safeguards)",
    "description": "Political rights and civil liberties ratings",
    "unit": "points",
    "direction": "higher",
    "validity": "Medium",
    "source": "FREEDOM_House",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "DEC_AGY_004",
    "name": "OECD AI Policy Observatory",
    "pillar": "Institutional_Governance",
    "subcomponent": "Decision agency (institutional safeguards)",
    "description": "AI governance framework strength",
    "unit": "index",
    "direction": "higher",
    "validity": "High",
    "source": "OECD_AI_Policy",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "DEC_AGY_005",
    "name": "Stanford AI Index Policy Section",
    "pillar": "Institutional_Governance",
    "subcomponent": "Decision agency (institutional safeguards)",
    "description": "AI regulation and policy tracking",
    "unit": "index",
    "direction": "higher",
    "validity": "Medium",
    "source": "STANFORD_AI_Index",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "ATT_WELL_001",
    "name": "Daily Screen Time Average",
    "pillar": "Wellbeing_Context",
    "subcomponent": "Attentional control / wellbeing",
    "description": "Average daily screen time across population",
    "unit": "hours",
    "direction": "lower",
    "validity": "Low",
    "source": "SCREENTIME_Owlnut",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "ATT_WELL_002",
    "name": "WHO Mental Health Prevalence",
    "pillar": "Wellbeing_Context",
    "subcomponent": "Attentional control / wellbeing",
    "description": "Depression and anxiety disorder prevalence",
    "unit": "%",
    "direction": "lower",
    "validity": "Medium",
    "source": "WELLBEING_WHO",
    "role": "Context-only",
    "normalization": "min-max"
  },
  {
    "code": "ATT_WELL_003",
    "name": "HBSC Adolescent Wellbeing",
    "pillar": "Wellbeing_Context",
    "subcomponent": "Attentional control / wellbeing",
    "description": "Student life satisfaction and wellbeing",
    "unit": "index",
    "direction": "higher",
    "validity": "Low",
    "source": "HBSC_2022",
    "role": "Rejected",
    "normalization": "min-max"
  },
  {
    "code": "VALUE_001",
    "name": "World Values Survey Tradition vs Secularism",
    "pillar": "Value_Clarity",
    "subcomponent": "Value clarity",
    "description": "Traditional vs secular-rational values",
    "unit": "index",
    "direction": "balanced",
    "validity": "Low",
    "source": "WVS_2023",
    "role": "Rejected",
    "normalization": "min-max"
  },
  {
    "code": "VALUE_002",
    "name": "EVS Values Importance Rankings",
    "pillar": "Value_Clarity",
    "subcomponent": "Value clarity",
    "description": "Importance placed on different values",
    "unit": "index",
    "direction": "balanced",
    "validity": "Low",
    "source": "EVS_2023",
    "role": "Rejected",
    "normalization": "min-max"
  },
  {
    "code": "VALUE_003",
    "name": "European Social Survey Values",
    "pillar": "Value_Clarity",
    "subcomponent": "Value clarity",
    "description": "Value importance and consistency measures",
    "unit": "index",
    "direction": "balanced",
    "validity": "Low",
    "source": "ESS_2023",
    "role": "Rejected",
    "normalization": "min-max"
  },
  {
    "code": "ENAB_001",
    "name": "Oxford Insights Government AI Readiness",
    "pillar": "Digital_Infrastructure",
    "subcomponent": "Enabling Environment",
    "description": "Comprehensive government AI readiness index",
    "unit": "index",
    "direction": "higher",
    "validity": "High",
    "source": "OXFORD_AI",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "ENAB_002",
    "name": "IMF AI Preparedness Index",
    "pillar": "Digital_Infrastructure",
    "subcomponent": "Enabling Environment",
    "description": "Assessment of AI readiness and adoption",
    "unit": "index",
    "direction": "higher",
    "validity": "High",
    "source": "IMF_AI",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "ENAB_003",
    "name": "ITU Development Index",
    "pillar": "Digital_Infrastructure",
    "subcomponent": "Enabling Environment",
    "description": "Infrastructure and connectivity readiness",
    "unit": "index",
    "direction": "higher",
    "validity": "High",
    "source": "ITU_DEVELOP",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "ENAB_004",
    "name": "World Bank Digital Adoption Index",
    "pillar": "Digital_Infrastructure",
    "subcomponent": "Enabling Environment",
    "description": "Digital technology adoption across economy",
    "unit": "index",
    "direction": "higher",
    "validity": "Medium",
    "source": "WBDIGITAL_WB",
    "role": "Retained",
    "normalization": "min-max"
  },
  {
    "code": "ENAB_005",
    "name": "WEF Technology Adoption Index",
    "pillar": "Digital_Infrastructure",
    "subcomponent": "Enabling Environment",
    "description": "Technology readiness and adoption",
    "unit": "index",
    "direction": "higher",
    "validity": "Medium",
    "source": "WEF_TECHADOPT",
    "role": "Retained",
    "normalization": "min-max"
  }
]