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While resilience in emergency management has been studied at the macro- (government) and micro-levels (individual field responder), little is known for resilience of incident management teams (IMTs). To investigate challenges and resilience factors of IMTs, this paper documents thematic analysis of 10 interviews with emergency personnel who responded to Hurricane Harvey in 2017. Themes emerged in four categories: goals, challenges, resilience factors, and technical tools of IMTs. Given similar goals but unique challenges during Harvey, IMTs sought to establish and maintain a common operating picture to make sense of evolving situations and make decisions adaptively. Various technical tools were used providing different functionalities, but a need for technology to reduce cognitive load was indicated. Findings of this study will inform the development of more resilient IMTs in future disasters.
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Cataloging Algorithmic Decision Making in the U.S. Government
Government use of algorithmic decision-making (ADM) systems is widespread and diverse, and holding these increasingly high-impact, often opaque government algorithms accountable presents a number of challenges. Some European governments have launched registries of ADM systems used in public services, and some transparency initiatives exist for algorithms in specific areas of the United States government; however, the U.S. lacks an overarching registry that catalogs algorithms in use for public-service delivery throughout the government. This paper conducts an inductive thematic analysis of over 700 government ADM systems cataloged by the Algorithm Tips database in an effort to describe the various ways government algorithms might be understood and inform downstream uses of such an algorithmic catalog. We describe the challenge of government algorithm accountability, the Algorithm Tips database and method for conducting a thematic analysis, and the themes of topics and issues, levels of sophistication, interfaces, and utilities of U.S. government algorithms that emerge. Through these themes, we contribute several different descriptions of government algorithm use across the U.S. and at federal, state, and local levels which can inform stakeholders such as journalists, members of civil society, or government policymakers
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- Award ID(s):
- 1845460
- PAR ID:
- 10386490
- Date Published:
- Journal Name:
- Computation + Journalism Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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We conducted an observational study to document student misconceptions about cybersecurity using thematic analysis of 25 think-aloud interviews. By understanding patterns in student misconceptions, we provide a basis for developing rigorous evidence-based recommendations for improving teaching and assessment methods in cybersecurity and inform future research. This study is the first to explore student cognition and reasoning about cybersecurity. We interviewed students from three diverse institutions. During these interviews, students grappled with security scenarios designed to probe their understanding of cybersecurity, especially adversarial thinking. We analyzed student statements using a structured qualitative method, novice-led paired thematic analysis, to document patterns in student misconceptions and problematic reasoning that transcend institutions, scenarios, or demographics. Themes generated from this analysis describe a taxonomy of misconceptions but not their causes or remedies. Four themes emerged: overgeneralizations, conflated concepts, biases, and incorrect assumptions. Together, these themes reveal that students generally failed to grasp the complexity and subtlety of possible vulnerabilities, threats, risks, and mitigations, suggesting a need for instructional methods that engage students in reasoning about complex scenarios with an adversarial mindset. These findings can guide teachers’ attention during instruction and inform the development of cybersecurity assessment tools that enable cross-institutional assessments that measure the effectiveness of pedagogies.more » « less
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Accepted Manuscript1.0
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