Suchergebnisse

Water treatment capability is required for military operations, production of drinking water, and industrial wastewater treatment. The Advanced Oxidation Process (AOP) is one of many viable steps for treating water; however, it is important to understand the byproducts of the treatment process to avoid creating other constituents more severe than the first. Tartrazine (TAR) was oxidized with pulsed Ultra- Violet (UV) Light Emitting Diodes (LEDs) in combination with hydrogen peroxide (H2O2) in an AOP at the laboratory scale. The relative concentration of TAR was reduced from 1 to between 1 – 0.75 over pH values between 6 and 9, and at Duty Cycles (DCs) ranging from 0% to100%. The first order rate constant for TAR removal was statistically and positively correlated with DC, was statistically and negatively correlated with pH, and was typically greatest at pH6. DC and pH were variables in a regression model of the first order rate constant with adjusted R2 of 0.85. Chromatographic contrast angle determinations revealed that the byproduct profile was most significantly influenced by pH 7 under both positive and negative ionization, by the 70% DC for the positive ionization, and 50% DC for the negative ionization. DC and pH were variables in regression models…

Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.

In examining the diffusion of social and political phenomena like regime transition, conflict, and policy change, scholars routinely make choices about how proximity is defined and which neighbors should be considered more important than others. Since each specification offers an alternative view of the networks through which diffusion can take place, one's decision can exert a significant influence on the magnitude and scope of estimated diffusion effects. This problem is widely recognized, but is rarely the subject of direct analysis. In international relations research, connectivity choices are usually ad hoc, driven more by data availability than by theoretically informed decision criteria. We take a closer look at the assumptions behind these choices, and propose a more systematic method to assess the structural similarity of two or more alternative networks, and select one that most plausibly relates theory to empirics. We apply this method to the spread of democratic regime change and offer an illustrative example of how neighbor choices might impact predictions and inferences in the case of the 2011 Arab Spring. Adapted from the source document.

This study addresses the factors that lead individuals to flee their homes in search of refuge. Many argue that individuals abandon their homes in favor of an uncertain life elsewhere because of economic hardship, while others argue that threats to their lives, physical person, and liberty cause them to flee. This study engages the debate by analyzing flight patterns over time from Haiti to the United States as a function of economic and security factors. Which factors have the largest influence on Haitian-U.S. migratory patterns? Our results show that both economics and security play a role. However, our analyses are able to distinguish between the effects of different individual economic and security indicators on Haitian-U.S. migration.

AbstractIn the past few decades new laws criminalizing certain transnational activities have proliferated: from money laundering, corruption, and insider trading to trafficking in weapons and drugs. Human trafficking is one example. We argue that criminalization of trafficking in persons has diffused in large part because of the way the issue has been framed: primarily as a problem of organized crime rather than predominantly an egregious human rights abuse. Framing human trafficking as an organized crime practice empowers states to confront cross-border human movements viewed as potentially threatening. We show that the diffusion of criminalization is explained by road networks that reflect potential vulnerabilities to the diversion of transnational crime. We interpret our results as evidence of the importance of context and issue framing, which in turn affects perceptions of vulnerability to neighbors' policy choices. In doing so, we unify diffusion studies of liberalization with the spread of prohibition regimes to explain the globalization of aspects of criminal law.

Social scientists commonly seek to make statements about how word use varies over circumstances—including time, partisan identity, or some other document-level covariate. For example, researchers might wish to know how Republicans and Democrats diverge in their understanding of the term "immigration." Building on the success of pretrained language models, we introduce the à la carte on text (conText) embedding regression model for this purpose. This fast and simple method produces valid vector representations of how words are used—and thus what words "mean"—in different contexts. We show that it outperforms slower, more complicated alternatives and works well even with very few documents. The model also allows for hypothesis testing and statements about statistical significance. We demonstrate that it can be used for a broad range of important tasks, including understanding US polarization, historical legislative development, and sentiment detection. We provide open-source software for fitting the model.

AbstractTopic models, as developed in computer science, are effective tools for exploring and summarizing large document collections. When applied in social science research, however, they are commonly used for measurement, a task that requires careful validation to ensure that the model outputs actually capture the desired concept of interest. In this paper, we review current practices for topic validation in the field and show that extensive model validation is increasingly rare, or at least not systematically reported in papers and appendices. To supplement current practices, we refine an existing crowd-sourcing method by Chang and coauthors for validating topic quality and go on to create new procedures for validating conceptual labels provided by the researcher. We illustrate our method with an analysis of Facebook posts by U.S. Senators and provide software and guidance for researchers wishing to validate their own topic models. While tailored, case-specific validation exercises will always be best, we aim to improve standard practices by providing a general-purpose tool to validate topics as measures.

Social scientists are now in an era of data abundance, and machine learning tools are increasingly used to extract meaning from data sets both massive and small. We explain how the inclusion of machine learning in the social sciences requires us to rethink not only applications of machine learning methods but also best practices in the social sciences. In contrast to the traditional tasks for machine learning in computer science and statistics, when machine learning is applied to social scientific data, it is used to discover new concepts, measure the prevalence of those concepts, assess causal effects, and make predictions. The abundance of data and resources facilitates the move away from a deductive social science to a more sequential, interactive, and ultimately inductive approach to inference. We explain how an agnostic approach to machine learning methods focused on the social science tasks facilitates progress across a wide range of questions.