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Full-Text Articles in Engineering

Strategic Implications For The United States Of The Belt And Road Initiative In Africa, Alec Monnie Oct 2019

Strategic Implications For The United States Of The Belt And Road Initiative In Africa, Alec Monnie

Independent Study Project (ISP) Collection

The belt and road initiative is one of the most significant developments of the twenty-first century, which the United States will need to learn to adapt to. Much of the academic literature regarding the belt and road initiative mentions that Africa is a significant participant in this policy development, but fail to elaborate as to why that is, or what the implications for this are for the United States. This article expands upon the strategic significance of the African continent, and explains the implications thereof for the United States and China. This article argues that the belt and road initiative …


Generational Aspects Of U.S. Public Opinion On Renewable Energy And Climate Change, Lawrence C. Hamilton, Joel N. Hartter, Erin Bell Jan 2019

Generational Aspects Of U.S. Public Opinion On Renewable Energy And Climate Change, Lawrence C. Hamilton, Joel N. Hartter, Erin Bell

Sociology

The topics of climate change and renewable energy often are linked in policy discussions and scientific analysis, but public opinion on these topics exhibits both overlap and divergence. Although renewable energy has potentially broader acceptance than anthropogenic climate change, it can also sometimes face differently-based opposition. Analyses of U.S. and regional surveys, including time series of repeated surveys in New Hampshire (2010–2018) and northeast Oregon (2011–2018), explore the social bases of public views on both issues. Political divisions are prominent, although somewhat greater regarding climate change. Such divisions widen with education, an interaction effect documented in other studies as well. …


Transparency And Algorithmic Governance, Cary Coglianese, David Lehr Jan 2019

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

All Faculty Scholarship

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose a …