Collaborative Research: An Expanded Framework for Inferring Public Policy Diffusion Networks

合作研究:推断公共政策扩散网络的扩展框架

基本信息

  • 批准号:
    1839282
  • 负责人:
  • 金额:
    $ 7.78万
  • 依托单位:
  • 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:
    2018-06-01 至 2022-05-31
  • 项目状态:
    已结题

项目摘要

General SummaryState governments are commonly referred to as "laboratories of democracy?"because for manydecades they have experimented with new policy solutions to society's most important problems.Public policy scholars and practitioners of politics often look to the states for innovative policiesthat can be emulated by other governments across the country; a process referred to as policydiffusion. However, the complex relationships between the states (that is, the patterns,pathways, and mechanisms underlying how policies actually diffuse from one state to another)are not well understood. In this research, the PIs develop innovative tools anddata to facilitate the large-scale computational and data-intensive study of policy diffusion. Theyseek to develop and utilize the largest database on states' decisions to adopt policies to betterunderstand which states are policy leaders, which are policy followers, and why. This willprovide a novel, expansive, and systematic view of the policy diffusion process that covers theAmerican states. As a result, policymakers and other interested stakeholders will have access to acomprehensive look at the spread of innovation among state governments regarding financial,environmental, health, security, and other social problem domains.Technical AspectsThis research seeks to make three central contributions to the study of public policy diffusion.First, it will produce a database on the adoption of hundreds of policies in the American states, atleast tripling the size of the largest database currently available and yielding a sample of policiesthat is more representative of the universe of policies for which states make laws. It will alsoleverage these new data to map and analyze the network pathways according to which policiesdiffuse (i.e., establish states that are leaders and followers in the diffusion of policies). Asrecently published research by the principal investigators shows, cutting-edge computationalmethods can be applied to large databases containing information on when policies were adoptedby which governments in order to identify underlying networks along which policies persistentlydiffuse. Another key contribution from this research is to improve upon the existing method forinferring network ties in ways that are most suitable for policy diffusion research (as well associal science more generally) and implement it in a user-friendly package in the R statisticalenvironment. A final contribution, which is intended to maximize the potential user-base of thesedata and methods, will be to build an interactive online portal to the data, complete withvisualizations and automated analytics.
一般摘要州政府通常被称为“民主实验室”,因为几十年来它们一直在尝试新的政策解决方案来解决社会最重要的问题。公共政策学者和政治实践者经常向各州寻求可以被其他国家效仿的创新政策。全国各地政府;这个过程被称为政策扩散。然而,国家之间的复杂关系(即政策实际上如何从一个国家扩散到另一个国家的模式、路径和机制)还没有得到很好的理解。在这项研究中,PI 开发了创新工具和数据,以促进政策扩散的大规模计算和数据密集型研究。他们寻求开发和利用关于各国采取政策决策的最大数据库,以更好地了解哪些国家是政策领导者,哪些国家是政策追随者,以及原因。这将为覆盖美国各州的政策扩散过程提供新颖、广泛和系统的视角。因此,政策制定者和其他感兴趣的利益相关者将能够全面了解各州政府在金融、环境、健康、安全和其他社会问题领域的创新传播情况。技术方面本研究旨在为该研究做出三个核心贡献首先,它将产生一个关于美国各州采用的数百项政策的数据库,该数据库的规模至少是目前可用的最大数据库的三倍,并产生更能代表其政策范围的政策样本。州制定法律。它还将利用这些新数据来绘制和分析政策传播的网络路径(即建立政策传播中的领导者和追随者的国家)。主要研究人员最近发表的研究表明,尖端的计算方法可以应用于包含哪些政府何时采用政策的信息的大型数据库,以便识别政策持续扩散的基础网络。这项研究的另一个重要贡献是以最适合政策扩散研究(以及更广泛的社会科学)的方式改进现有的网络关系推断方法,并在 R 统计环境中以用户友好的包实施它。最后的贡献旨在最大限度地利用这些数据和方法的潜在用户群,将构建一个交互式数据在线门户,并提供可视化和自动分析。

项目成果

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Hanna Wallach其他文献

Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing an AI Fairness Checklist
修补、定制、配置、定制:人工智能公平性检查表情境化的阐明工作
A Diffusion Network Event History Estimator
扩散网络事件历史估计器
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    Jeffrey J. Harden;Bruce A. Desmarais;Mark Brockway;F. Boehmke;Scott J. LaCombe;Fridolin Linder;Hanna Wallach
  • 通讯作者:
    Hanna Wallach
E-MMAD: Multimodal Advertising Caption Generation Based on Structured Information
E-MMAD:基于结构化信息的多模态广告字幕生成
  • DOI:
  • 发表时间:
    2024-09-14
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Wei;D. Jiang;Hsiang;David Chen;William B Dolan. 2011;Collecting;Zhiyu Chen;H. Eavani;Wenhu Chen;J. Devlin;Ming;Kenton Lee;Timnit Gebru;Jamie Morgenstern;Briana Vecchione;Jennifer Wortman Vaughan;Hanna Wallach;Shuiwang Ji;Wei Xu;Ming Yang;Kai Yu;Ranjay Krishna;K. Hata;Frederic Ren;Fei
  • 通讯作者:
    Fei
Opportunities for Machine Learning Research to Support Fairness in Industry Practice
机器学习研究支持行业实践公平的机会
  • DOI:
    10.2139/ssrn.3740577
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kenneth Holstein;Jennifer Wortman;Hal Daumé;Miro Dudik;Hanna Wallach
  • 通讯作者:
    Hanna Wallach
The KITMUS Test for Knowledge Integration from Multiple Sources
KITMUS 多源知识整合测试
  • DOI:
    10.48550/arxiv.2404.05904
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tom Brown;Benjamin Mann;Nick Ryder;Melanie Subbiah;Jared Kaplan;Prafulla Dhariwal;Arvind Neelakantan;Pranav Shyam;Girish Sastry;Amanda Askell;Sandhini Agarwal;Ariel Herbert;Gretchen Krueger;T. Henighan;R. Child;Aditya Ramesh;Daniel M. Ziegler;Jeffrey Wu;Clemens Winter;Chris Hesse;Mark Chen;Eric Sigler;Mateusz Litwin;S. Gray;B. Chess;J. Clark;Christopher Berner;Sam McCandlish;Alec Radford;I. Sutskever;Dario Amodei. 2020;J. L. Fleiss;Bruce Levin;Myunghee Cho;J. Wiley;Ltd Sons;Timnit Gebru;Jamie Morgenstern;Briana Vecchione;Jennifer Wortman Vaughan;Hanna Wallach;Mandar Joshi;Omer Levy;Luke Zettlemoyer;Jungo Kasai;Robert Frank. 2019. Jabberwocky;Irene Solaiman;M. Brundage;Ariel Askell;Jeff Herbert;Alec Wu;Radford;Jong Wook Kim;Sarah Kreps;Shubham Toshniwal;Allyson Ettinger;Kevin Gimpel;Ashish Vaswani;Noam M. Shazeer;Niki Parmar;Kellie Webster;Marta Recasens;Vera Axelrod;Jason Weston;Antoine Bordes;S. Chopra;M. Rush;Bart van Merriënboer;Armand Joulin
  • 通讯作者:
    Armand Joulin

Hanna Wallach的其他文献

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{{ truncateString('Hanna Wallach', 18)}}的其他基金

Collaborative Research: An Expanded Framework for Inferring Public Policy Diffusion Networks
合作研究:推断公共政策扩散网络的扩展框架
  • 批准号:
    1558781
  • 财政年份:
    2016
  • 资助金额:
    $ 7.78万
  • 项目类别:
    Standard Grant
III: Small: Organizational Responsiveness to Open Outside Input: A Modeling Approach based on Statistical Text and Network Analysis
III:小:组织对开放外部输入的响应:基于统计文本和网络分析的建模方法
  • 批准号:
    1320219
  • 财政年份:
    2013
  • 资助金额:
    $ 7.78万
  • 项目类别:
    Continuing Grant
Collaborative Research: Workshop for Women in Machine Learning
合作研究:机器学习领域女性研讨会
  • 批准号:
    1037002
  • 财政年份:
    2010
  • 资助金额:
    $ 7.78万
  • 项目类别:
    Standard Grant
Collaborative Research: New Methods to Enhance Our Understanding of the Diversity of Science
合作研究:增强我们对科学多样性理解的新方法
  • 批准号:
    0965436
  • 财政年份:
    2010
  • 资助金额:
    $ 7.78万
  • 项目类别:
    Standard Grant

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