Beth-Anne Schuelke-Leech

The human dimensions of climate change: A micro-level assessment of views from the ecological modernization, political economy and human ecology perspectives.

Understanding the manifold human and physical dimensions of climate change has become an area of great interest to researchers in recent decades. Using a U.S. nationally-representative data set and drawing on the ecological modernization, political economy, and human ecology perspectives, this study examines the impacts of energy efficiency technologies, affluence, household demographics, and biophysical characteristics on residential CO2 emissions. Overall, the study provides mixed support for the ecological modernization perspective. While several findings are consistent with the theorys expectation that modern societies can harness technology to mitigate human impacts on the environment, others directly contradict it. Also, the theorys prediction of an inverted U-shaped relationship between affluence and environmental impacts is contradicted. The evidence is somewhat more supportive of the political economy and human ecology perspectives, with affluence, some indicators of technology, household demographics, and biophysical characteristics emerging as important drivers of residential CO2 emissions.

Complexity of Textual Data in Entrepreneurship and Innovation Research

Innovation and Entrepreneurship are complex activities. They are also primarily language and relationship based. That is, it is largely through verbal communications (speech and text) that ideas are developed and business transacted. New methods are arising which are changing the way that we understand and can investigate innovation and entrepreneurship. Big Data Analytics allow researchers to uncover relationships and meaning in text documents, using a mix of quantitative and qualitative methods. This chapter shows that the complexity issues in innovation and entrepreneurship research with text comes from three sources. The first form of complexity is technical complexity. The second source of complexity is from language itself. The third source of complexity is in the concept itself. Each of these is discussed in detail. Complexity can either be addressed by simplifying the data or finding a mechanism for dealing with the complexity. A method of text data analytics using Corpus and Computational Linguistics deals with the complexity without eliminating data, allowing for a more nuanced investigation of innovation and entrepreneurship. The methodology is demonstrated by investigating how technological innovation and entrepreneurship are discussed in the United States Congress, using a corpus from 1981 to 2014.

Speaking Like Statesmen or Scientists: Differentiating Congressional and Administrative Views on Data

ABSTRACT Do legislators and executives speak of data the same way when speaking about public sector data? Public management scholarship and public performance policies often emphasize data-driven decision making as the path to making government efficient and effective. Whether the public policy makers mean the same thing when they speak about data in discussions of data-driven performance and decision making is unknown. In this article, the authors present an analysis of the language of data in conversations about government performance. Two frameworks are identified for the role of data in public performance—the statesman’s and the scientist’s. A corpus-level analysis of over 30 years of government documents is used to demonstrate the differences between these two approaches. This research builds consciously on the work of previous scholars seeking to map the nuances of data-driven performance management policies in the U.S. federal government.