Ready or Not? Protecting the Public's Health From Diseases, Disasters, and Bioterrorism, 2008

Examines ten indicators to assess progress in state readiness to respond to bioterrorism and other public health emergencies. Evaluates the federal government's and hospitals' preparedness. Makes suggestions for funding, restructuring, and other reforms

LOCATION MENTION PREDICTION FROM DISASTER TWEETS

While utilizing Twitter data for crisis management is of interest to different response authorities, a critical challenge that hinders the utilization of such data is the scarcity of automated tools that extract and resolve geolocation information. This dissertation focuses on the Location Mention Prediction (LMP) problem that consists of Location Mention Recognition (LMR) and Location Mention Disambiguation (LMD) tasks. Our work contributes to studying two main factors that influence the robustness of LMP systems: (i) the dataset used to train the model, and (ii) the learning model. As for the training dataset, we study the best training and evaluation strategies to exploit existing datasets and tools at the onset of disaster events. We emphasize that the size of training data matters and recommend considering the data domain, the disaster domain, and geographical proximity when training LMR models. We further construct the public IDRISI datasets, the largest to date English and first Arabic datasets for the LMP tasks. Rigorous analysis and experiments show that the IDRISI datasets are diverse, and domain and geographically generalizable, compared to existing datasets. As for the learning models, the LMP tasks are understudied in the disaster management domain. To address this, we reformulate the LMR and LMD modeling and evaluation to better suit the requirements of the response authorities. Moreover, we introduce competitive and state-of-the-art LMR and LMD models that are compared against a representative set of baselines for both Arabic and English languages

What FEMA Should Do After Puerto Rico: Toward Critical Administrative Constitutionalism

The 200th anniversary of the 1819 Supreme Court decision McCulloch v. Maryland offers scholars a special opportunity to study the shortcomings of the federal The Robert T. Stafford Disaster Relief and Emergency Assistance Act, as they were revealed by FEMA’s failures in Puerto Rico during and after Hurricane Maria. Under Article I, Section 8 of the Constitution, as it has been interpreted by McCulloch, a law passed by Congress must be necessary and proper for executing its powers. In light of the expansive capacities allotted for disaster relief under the Stafford Act, and the catastrophic failure of FEMA to provide meaningful aid to vulnerable populations in Puerto Rico in the Fall of 2017, scholars of race and class justice are faced with a melancholy-inducing reminder: Even a well-drafted law passed by Congress for the betterment of the general welfare may be “necessary,” but not rise to the level of “proper.” Meaningfully, the test for propriety here grows out of by McCulloch’s demand that laws cohere to the “letter and spirit” of the Constitution, as well as anti-subordination interpretations of the Fifth and Fourteenth Amendments that require equal protection to be pursued in light of the lived experience of vulnerable and minority populations

Ready or Not? Protecting the Public\u27s Health from Diseases, Disasters, and Bioterrorism

While significant progress has been made to better protect the country from health emergencies, funding for essential programs has been cut, putting these improvements in jeopardy. Additionally, a number of critical areas of preparedness still have significant gaps, including surge capacity and biosurveillance systems, and these problems are less likely to be addressed as funding decreases

Hidden Power: Journalistic Representations of Mental Health Labels

Individuals with disabilities make up the largest minority group in the U.S., and the language used to construct representations of these individuals has the ability to perpetuate or diminish stereotypes about these individuals. The purpose of this case study was to explore and describe the representations of mental health in online newspaper articles published by three national publications – The Washington Post, The New York Times, and USA Today. Critical Discourse Analysis (CDA) was used as the methodological framework, including an analysis of semiotic choices, dominant perspectives, and causality. The case study allowed for data collection using the key terms mental health and mental illness from the three online newspapers, with a total of 33 articles published between July 1, 2018 and December 31, 2018. The findings identified that most discussions of mental health and mental illness align with a medical model frame and incorporate medicalized lexicon. Dominant perspectives of causality within articles remain with law enforcement, lawmakers, and legal advisors. Overlexicalization was evident, and the use of mental health and mental illness was more often stated with semi-formal or formal language. Findings also suggest that individuals with mental health labels are often labeled as an aggressor with specific individuals or local citizens as their victims. Potential ramifications of hidden power, as well as recommendations on altering the use of the key terms and sources used within an article are discussed

Understanding and Modeling Risk and Resilience in Complex Coastal Systems (final workshop report)

On October 29 & 30, 2014 SURA hosted an interdisciplinary workshop on the subject, Understanding and Modeling Risk and Resilience in Complex Coastal Systems, as a prelude to a new SURA-led community science initiative integrating social and natural science. The goals were to identify the most critical issues in assessing future risks, vulnerabilities and resilience of complex coastal systems