危机信息管理中强化数据偏差:以也门人道主义应对为例

IF 20.1 1区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
David Paulus , Gerdien de Vries , Marijn Janssen , Bartel Van de Walle
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引用次数: 1

摘要

人道主义应对危机的复杂和不确定环境可能导致数据偏差,从而影响决策。尽管危机信息管理(CIM)可能对危机应对产生重大影响,但其数据偏见的证据仍然零散。为了了解复杂危机中出现的偏见以及它们如何影响CIM,我们进行了一项访谈和文件分析相结合的研究。围绕世界上最大的人道主义危机,即也门冲突,我们对应对组织的管理人员和分析师进行了25次采访,并评估了也门应对组织创建的47份报告和数据集。我们发现了偏见强化循环的证据,通过这种循环,偏见在外地、总部和捐助者层面的危机应对之间级联。研究人员和从业者都需要考虑这些潜在的偏见和强化循环,因为它们会影响何时、由谁、从谁那里收集数据,以及如何共享和使用数据。在CIM文献中,我们深入了解了四种类型的数据偏见是如何在危机中出现的:政治性、可访问性、主题性和抽样偏见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforcing data bias in crisis information management: The case of the Yemen humanitarian response

The complex and uncertain environment of the humanitarian response to crises can lead to data bias, which can affect decision-making. Evidence of data bias in crisis information management (CIM) remains scattered despite its potentially significant impact on crisis response. To understand what biases emerge in complex crises and how they affect CIM, we conducted a combined interview and document analysis study. Focusing on the largest humanitarian crisis in the world, i.e., the conflict in Yemen, we conducted 25 interviews with managers and analysts of response organizations, and assessed 47 reports and datasets created by response organizations in Yemen. We find evidence of a cycle of bias reinforcement through which bias cascades between field, headquarters and donor levels of crisis response. Researchers, as well as practitioners, need to consider these underlying biases and reinforcement loops because they influence what data can be collected when, by whom, from whom, and how the data is shared and used. To the CIM literature, we contribute an in-depth understanding of how four types of data bias emerge in crises: political, accessibility, topical, and sampling bias.

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来源期刊
International Journal of Information Management
International Journal of Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
53.10
自引率
6.20%
发文量
111
审稿时长
24 days
期刊介绍: The International Journal of Information Management (IJIM) is a distinguished, international, and peer-reviewed journal dedicated to providing its readers with top-notch analysis and discussions within the evolving field of information management. Key features of the journal include: Comprehensive Coverage: IJIM keeps readers informed with major papers, reports, and reviews. Topical Relevance: The journal remains current and relevant through Viewpoint articles and regular features like Research Notes, Case Studies, and a Reviews section, ensuring readers are updated on contemporary issues. Focus on Quality: IJIM prioritizes high-quality papers that address contemporary issues in information management.
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