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

社会内部和社会之间的不平等处于学术和政策讨论的前沿，这些讨论涉及暴力极端主义的兴起、国内冲突的升级和蔓延以及环境冲击的潜在解决方案带来的全球范围的挑战。新的概念性研究表明，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，即横向不平等(HIS)，是解释种族冲突和不稳定的关键机制。然而，衡量不同和重叠的群体、权利和资源以及社会背景的横向不平等的复杂性，阻碍了科学理解和政策制定者干预和减轻HIS可能破坏稳定的后果的能力。Triad(跟踪各个维度的横向不平等以预测和了解不稳定)通过将来自社会、信息和计算机科学的学者联系起来，并利用与政治、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，并发展在数据科学，特别是计算社会科学方面备受欢迎的技能。该项目创造的知识和技术为新兴的数据驱动型社会持续发展技能奠定了基础。TrIAD(跟踪各维度的横向不平等以预测和了解不稳定)独特地将人权衡量工作与横向不平等(HIS)的概念联系起来。三合会特别利用稀疏和流动的大规模人权数据，使用信息科学方法、贝叶斯模型和高性能计算的进步，以了解横向不平等如何以及在哪里导致不稳定升级，并发现对抗这种暴力的新手段。利用人权文本分析器Pulsar，三合会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编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，三合会准确地确定了施暴者和特定的受害者，并对正在受到侵犯的人权/资源进行了编码。三合会还包括一个建模和分析框架，可以推断不满的潜在结构，以及它们如何在不同的背景下投射到暴力中。完成的三合会为研究人员和政策制定者提供了高度分类的数据，涉及不同社会群体对100多种资源和权利的系统性不平等获取，包括随时间和空间变化的显著种族、语言、宗教、地区、部族和意识形态划分。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。

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.