Tracking Horizontal Inequalities Across Dimensions to Forecast and Understand Instability (TrIAD)

跟踪跨维度的水平不平等以预测和了解不稳定性 (TrIAD)

• 批准号: 2017614
• 负责人: Michael Colaresi
• 金额: $ 40.8万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2017614&HistoricalAwards=false
• 关键词: Tracking; Horizontal; Inequalities; Across; Dimensions

Inequality within and across societies is at the forefront of academic and policy discussions related to global-scale challenges from the rise of violent extremism, the escalation and spread of civil conflict, and potential solutions to environmental shocks. New conceptual research suggests that group-based grievances about access to rights and resources, known as horizonal inequalities (HIs), as compared to individual-to-individual vertical inequalities, are crucial mechanisms that explain ethnic conflict and instability. However, the complexity of measuring horizontal inequalities across different and overlapping groups, rights and resources, and societal contexts, has impeded scientific understanding and policy-makers’ ability to intervene and mitigate the potentially destabilizing consequences of HIs. TrIAD (Tracking Horizontal Inequalities Across Dimensions to Forecast and Understand Instability) overcomes previous hurdles by linking scholars from the social, information, and computer sciences, as well as drawing on expertise related to political, security, and historical dimensions. Furthermore, the project provides a fertile environment for students with diverse backgrounds to gain high-quality research experience and develop highly sought-after skills in data science, in general, and computational social science in particular. The knowledge and technologies created from this project lay the foundation for the sustained development of skills for the emerging data-driven society.TrIAD (Tracking Horizontal Inequalities Across Dimensions to Forecast and Understand Instability) uniquely connects work on human rights measurement to the concept of horizonal inequalities (HIs). TrIAD particularly harnesses the sparse and streaming large-scale data on human rights, using advances in information-science methods, Bayesian models, and high-performance computing, to understand how and where horizonal inequalities lead to escalating instability and discover new means of counteracting this violence. Using PULSAR, a human rights text parser, TrIAD is able to extract structured information from the streams of raw textual information authored and published by hundreds of human rights non-governmental organization in press releases and formal reports daily. From these human rights documents, TrIAD accurately identifies the perpetuators and specific victims, as well as codes the human right/resource that is being violated. TrIAD also includes a modeling and analysis framework that infers the latent structure of grievances and how they project into violence across different contexts. The completed TrIAD provides researchers and policy-makers with highly disaggregated data on systematically unequal access to over 100 resources and rights, across different societal groupings, including salient racial, linguistic, religious, regional, clan, and ideological partitions that vary over time and space.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.

社会内部和社会之间的不平等是学术和政策讨论的前沿，这些讨论涉及暴力极端主义抬头、国内冲突升级和蔓延以及环境冲击的潜在解决方案等全球范围的挑战。新的概念研究表明，与个人与个人之间的纵向不平等相比，基于群体的对获得权利和资源的不满，即所谓的种族不平等，是解释种族冲突和不稳定的关键机制。然而，衡量不同和重叠群体、权利和资源以及社会背景之间的横向不平等的复杂性，阻碍了科学理解和决策者干预和减轻人道主义影响的潜在不稳定后果的能力。TrIAD（Tracking Horizontal Inequalities Across Dimensions to Forecast and Understability）通过将来自社会、信息和计算机科学的学者联系起来，以及利用与政治、安全和历史维度相关的专业知识，克服了以前的障碍。此外，该项目为具有不同背景的学生提供了一个肥沃的环境，以获得高质量的研究经验，并在数据科学，特别是计算社会科学方面发展备受追捧的技能。该项目创造的知识和技术为新兴的数据驱动型社会的技能持续发展奠定了基础。TrIAD（Tracking Horizontal Inequalities Across Dimensions to Forecast and Understability）将人权衡量工作与区域不平等概念独特地联系起来。TrIAD特别利用关于人权的稀疏和流动的大规模数据，利用信息科学方法，贝叶斯模型和高性能计算的进步，了解地区不平等如何以及在何处导致不稳定升级，并发现对抗这种暴力的新方法。利用人权文本分析器PULSAR，TrIAD能够从数百个人权非政府组织每天在新闻稿和正式报告中撰写和发表的原始文本信息流中提取结构化信息。从这些人权文件中，TrIAD准确地确定了侵权者和具体受害者，并对受到侵犯的人权/资源进行了编码。TrIAD还包括一个建模和分析框架，该框架推断了不满的潜在结构以及它们如何在不同背景下投射为暴力。已完成的TrIAD为研究人员和政策制定者提供了高度分类的数据，说明不同社会群体在获得100多种资源和权利方面的系统性不平等，这些群体包括突出的种族、语言、宗教、地区、氏族、该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值进行评估来支持和更广泛的影响审查标准。