Analyzing NGO Communications to Understand Contestation and Collaboration with Governments Across Human Rights Issues
Analyzing NGO Communications to Understand Contestation and Collaboration with Governments Across Human Rights Issues
批准号:
1753528
负责人:
Michael Colaresi
金额:
$15.76万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-07-01 至 2020-05-31
中文摘要
红十字国际委员会和大赦国际等人权组织使用一系列战略来改善世界各地的人权和社会状况。一些策略采取公开和直接批评国家的形式,以惩罚和威慑对公民的镇压,即所谓的“点名羞辱”。其他战略包括与各国政府积极合作,以改变对权利和自由的法律和事实上的保护。到目前为止,我们对哪种战略更有效知之甚少,因为对这一主题的系统研究忽视了这些组织和国家之间的直接合作。相反,最近研究有争议策略的有效性的社会科学研究呈爆炸式增长。这项研究将为这种合作战略是否是改善人权和社会条件的有效手段提供新的见解。该项目是第一个对人权非政府组织的大规模数字通信进行分析的项目,以了解可以成功开展合作的具体问题和背景。研究人员将通过定量文本分析程序分析各种数字可用的信息(即新闻稿、已发布的报告、时事通讯和来自多个人力资源非政府组织的社交媒体帖子),证明这种方法的有效性。这些是一个特别丰富的信息来源,因为它们既包含对各国的合作信号,也包含对各国的对抗信号。研究小组计划建立理论上知情的模型,以预测1)为政府和人力资源非政府组织伙伴关系提供激励而不是公众批评的问题和情况,即所谓的“点名和羞辱”,以及2)特定的人力资源非政府组织战略最终导致国家行为的预期变化。这项研究通过更好地了解改善世界各地人权状况的战略,有可能增进社会福祉。它还准备在帮助促进社会科学和其他STEM学科之间的合作方面做出重要贡献。
英文摘要
Human rights organizations (HR NGOs), such as the International Committee of the Red Cross and Amnesty International, use a range of strategies to improve human rights and social conditions around the world. Some strategies take the form of overt and direct criticism of states to punish and deter repression of citizens, known as "naming and shaming". Other strategies involve positive cooperation with governments to change legal and de facto protections of rights and freedoms. To date, we know little about which strategy is more effective, because systematic research on this topic has ignored direct collaboration between these organizations and states. Instead, there has been a recent explosion in the number of social science studies examining the efficacy of contentious strategies. This research will provide new insight into whether such cooperative strategies are an effective means to improve human rights and social conditions. This project is the first to analyze the large-scale digital communications from human rights non-governmental organizations (HR NGOs) to understand the specific issues and contexts where cooperation can be successfully implemented. The researchers will demonstrate the usefulness of this approach through quantitative text analysis procedure analyzing a wide range of digitally available information (i.e. press releases, published reports, newsletters, and social media posts from multiple HR NGOs). These are an especially rich source of information as they contain both cooperative and confrontational signals to states. The research team plans to build theoretically informed models that predict 1) the issues and circumstances that provide incentives for government-HR NGO partnerships versus public criticism known as "naming and shaming," and 2) when particular HR NGO strategies ultimately lead to the desired changes in state behavior. This research has the potential to enhance the well-being of society by providing a better understanding of strategies that improve human rights performance around the world. It is also poised to make an important contribution in helping to foster collaboration between the social sciences and other STEM disciplines.
期刊论文(4)
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科研奖励(0)
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DOI:
10.1080/14754835.2019.1671174
发表时间:
2020-01
期刊:
Journal of Human Rights
影响因子:
1.9
作者:
[Baekkwan Park;Kevin Greene;Michael Colaresi]
通讯作者:
Baekkwan Park;Kevin Greene;Michael Colaresi
DOI:
10.1515/peps-2018-0030
发表时间:
2018-10
期刊:
Peace Economics, Peace Science and Public Policy
影响因子:
--
作者:
[Baekkwan Park;Michael Colaresi;Kevin Greene]
通讯作者:
Baekkwan Park;Michael Colaresi;Kevin Greene
Human Rights are (Increasingly) Plural: Learning the Changing Taxonomy of Human Rights from Large-scale Text Reveals Information Effects
人权(越来越)多元化:从大规模文本中学习不断变化的人权分类法揭示了信息效应
DOI:
10.1017/s0003055420000258
发表时间:
2020
期刊:
American Political Science Review
影响因子:
6.8
作者:
[PARK, BAEKKWAN, GREENE, KEVIN, COLARESI, MICHAEL]
通讯作者:
COLARESI, MICHAEL
Machine Learning Human Rights and Wrongs: How the Successes and Failures of Supervised Learning Algorithms Can Inform the Debate About Information Effects
机器学习人权与错误:监督学习算法的成功和失败如何为有关信息效应的争论提供信息
DOI:
10.1017/pan.2018.11
发表时间:
2019
期刊:
Political Analysis
影响因子:
5.4
作者:
[Greene, Kevin T., Park, Baekkwan, Colaresi, Michael]
通讯作者:
Colaresi, Michael
Tracking Horizontal Inequalities Across Dimensions to Forecast and Understand Instability (TrIAD)
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批准号:2017614
-
项目类别:Standard Grant
-
资助金额:$40.8万
-
财政年份:2020
-
负责人:Michael Colaresi
-
依托单位:
Analyzing NGO Communications to Understand Contestation and Collaboration with Governments Across Human Rights Issues
-
批准号:1657700
-
项目类别:Standard Grant
-
资助金额:$15.76万
-
财政年份:2017
-
负责人:Michael Colaresi
-
依托单位:
国内基金
海外基金
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