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Full-Text Articles in Public Administration
A Transnational Network For Public Sector Innovation: The Impact Of A Global Digital Government Reform Network On Public Administration At The Domestic Level, Beomgeun Cho, R. Karl Rethemeyer
A Transnational Network For Public Sector Innovation: The Impact Of A Global Digital Government Reform Network On Public Administration At The Domestic Level, Beomgeun Cho, R. Karl Rethemeyer
Research Collection School of Social Sciences
This study investigates the impact of a global E-government reform network on an individual country's E-government performance. As keeping pace with changing environments becomes one of the essential tasks for governments to retain problem-solving capacity, scholars have paid a lot of attention to the determinants of public sector innovation. However, how the ideas of reform and innovation have been communicated at the international or intergovernmental level has been paid less attention. To fill the gap in the literature, we have constructed a social network dataset covering 179 countries for the period 2010 to 2013. This dataset records whether countries sent …
Transparency And Algorithmic Governance, Cary Coglianese, David Lehr
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 …