Improving and Integrating Global Diversity Estimates Using Transparent Methods

使用透明方法改进和整合全球多样性估计

• 批准号: 1824005
• 负责人: Avital Livny
• 金额: $ 31.56万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824005&HistoricalAwards=false
• 关键词: Improving; Integrating; Global; Diversity; Estimates

Understanding the socio-political impact of ethnic, religious, and linguistic diversity is a vital concern for both social scientists and policy-makers. Using several different datasets measuring diversity around the world, researchers have found that it often has a negative impact on democracy, development and political stability. However, these findings rely on fundamentally flawed datasets: their demographic estimates come from questionable sources and do not vary within countries or over time. This makes them ill-suited to studying outcomes like war and development, which often change rapidly and cluster in space. In addition, the existing datasets cannot be directly compared, making it difficult to assess their accuracy. Rather than investing time and money in the collection of new data, this project builds on an existing resource: 9 million survey responses from 175 countries around the world. By using Artificial Intelligence (AI) methods to compare these surveys to official statistics and generate improved diversity estimates, other researchers will be able to apply this method in other areas like health and inequality where government statistics are missing or questionable. Further, this project will make our results directly comparable to existing datasets. These data and methods will be made available through a user-friendly online portal, where scientists, policy-makers, and members of the public will be able to explore the data. They will also be able to create their own datasets and use visualization tools to see how the world's demographics are changing and consider what this means for our future.Existing estimates of ethnic, religious, and linguistic diversity are correlated cross-sectionally with a number of socio-political and economic outcomes including development, conflict, and social capital. Close examination of these data raises validity concerns: few are based on high-quality official statistics, the majority coming from questionable secondary sources. Further, criteria for group inclusion (i.e., ontologies) are opaque and inconsistently applied. Even where they appear accurate, data are static and aggregated at the country level, although they are often used to explain time-varying and spatially disaggregated outcomes. Ontologies in extant datasets are also incompatible, making comparison and integration difficult. This proposal improves existing measures by applying machine learning methods to compare 9 million responses across 175 countries with a new database of census results. An algorithm will identify survey design features that maximize accuracy, to define a compensatory weighting scheme across these features. The result is a set of survey-based demographic estimates with improved validity, even for countries lacking reliable census data. This method of triangulating surveys and official statistics is generalizable to research areas that use either source and can also inform improved survey design. The project will also develop tools for linking surveys, censuses, and existing datasets based on explicit and transparent decision rules to facilitate their comparison and integration. An online portal will provide access to datasets and code, supporting customized data manipulation and visualization. The methods and tools proposed here -- emphasizing accuracy, transparency, and cross-resource integration -- should serve as a model for future data collection.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对于社会科学家和政策制定者来说，理解种族、宗教和语言多样性的社会政治影响是一个至关重要的问题。研究人员使用几个不同的数据集来衡量世界各地的多样性，发现它往往对民主、发展和政治稳定产生负面影响。然而，这些发现依赖于从根本上有缺陷的数据集：他们的人口估计来自可疑的来源，在国家内部或时间上没有变化。这使得他们不适合研究战争和发展等结果，这些结果往往变化迅速，并在太空中聚集。此外，现有的数据集不能直接进行比较，因此很难评估其准确性。该项目没有在收集新数据上投入时间和金钱，而是建立在现有资源的基础上：来自全球175个国家的900万份调查答复。通过使用人工智能(AI)方法将这些调查与官方统计数据进行比较并生成改进的多样性估计，其他研究人员将能够将这种方法应用于政府统计数据缺失或可疑的其他领域，如卫生和不平等领域。此外，这个项目将使我们的结果直接与现有的数据集相比较。这些数据和方法将通过一个用户友好的在线门户网站提供，科学家、政策制定者和公众将能够在那里探索这些数据。他们还将能够创建自己的数据集，并使用可视化工具来查看世界人口结构是如何变化的，并考虑这对我们的未来意味着什么。现有的种族、宗教和语言多样性估计与包括发展、冲突和社会资本在内的许多社会政治和经济结果横向相关。对这些数据的仔细检查引发了人们对有效性的担忧：很少有数据是基于高质量的官方统计数据，而大多数数据来自可疑的二手来源。此外，组包含的标准(即，本体论)是不透明的，并且应用不一致。即使在数据看似准确的地方，数据在国家一级也是静态的和汇总的，尽管它们经常被用来解释时变的和按空间分列的结果。现有数据集中的本体也是不兼容的，这使得比较和集成变得困难。这项提议通过应用机器学习方法改进现有措施，将175个国家的900万份答复与一个新的人口普查结果数据库进行比较。一种算法将识别最大精度的测量设计特征，以定义跨这些特征的补偿性加权方案。其结果是一套基于调查的人口估计，其有效性得到了提高，即使是对缺乏可靠人口普查数据的国家也是如此。这种三角测量调查和官方统计数据的方法可推广到使用任何一种来源的研究领域，也可以为改进调查设计提供信息。该项目还将开发工具，根据明确和透明的决策规则将调查、人口普查和现有数据集联系起来，以促进它们的比较和整合。一个在线门户将提供对数据集和代码的访问，支持定制的数据操作和可视化。这里提出的方法和工具--强调准确性、透明度和跨资源整合--应该成为未来数据收集的典范。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。