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Call for Papers

Call for Papers: Contesting the Norms in Public Administration

Big data. Evidence-based policy. Performance measurement. Transparency. Citizen engagement. Accountability. Coproduction. Social justice.

The list could go on, but there often is an embrace in the field of the latest and greatest buzzword or tool. Frederickson (Citation1996) asked whether reinventing government was old wine in new bottles, concluding “mostly yes” (p. 269). He continues, noting, “each generation must construct its own reality. There is probably not much new under the sun, but, to each new generation, many things seem importantly new and useful” (Frederickson, Citation1996, p. 269). Schachter (Citation2007) asks the same questions about performance measurement and efficiency, arguing that much of that movement find its roots in Frederick Taylor’s scientific management. Heidelberg (Citation2018) deconstructs accountability, offering ways in which accountability is incongruous with self-government.

This call for papers seeks scholarship that contests the field’s norms, that offer a fresh perspective on some of the latest buzzwords and methodologies. Papers could consider the following questions, but other topics are of course welcome.

  • Social Justice. What theoretical foundations can be used to understand social justice in its historical context? Has that context changed too much? How, if at all, does the blending of social justice and social equity affect the term’s use in theory and practice? How could elites be coopting the term as a mechanism to maintain the status quo? What happens when the language of social justice is adopted rather than actions?

  • Transparency. What are the underlying normative assumptions about transparency, and how do those help or hinder public administration? When would transparency be a negative value? What are the costs, both monetary and social, of transparency?

  • Big data. Could big data be causing more harm than good? What are privacy concerns related to the use of big data? How can big data improve citizen participation? What exactly is big data, and what is not big data? What are some theoretical foundations we can use to understand the effects of big data on public administration and the public?

  • Experimental design. What are some theoretical and methodological limitations of experimental designs in public administration? Is behavioral public administration old wine in new bottles? What makes behavioral public administration different from other methodologies? What theoretical foundations can we use to understand the influence of behavioral public administration?

  • Evidence-based policy. What is evidence? What does not count as evidence, and does that matter for theoretical and practical development of the field? What is the role of stories in evidence-based policymaking?

  • Global concern. Perhaps this concern with evidence qua numbers is a Western push, so what could a comparative perspective tell us when it comes to contesting the field’s norms? What other non-Western norms or theories can inform contemporary public administration theory and practice?

Abstracts should be sent to Editor-in-Chief Dr. Staci Zavattaro by October 1, 2019. Invitations to submit full papers must be received by November 1, 2019. Full papers will be due on March 1, 2020. Invitation to submit a manuscript does not guarantee publication. Authors should submit manuscripts to www.editorialmanager.com/atp. Please contact [email protected] with questions.

References

  • Frederickson, H. G. (1996). Comparing the reinventing government movement with the New Public Administration. Public Administration Review, 56(3), 263–270. doi:10.2307/976450
  • Heidelberg, R. (2018). Ten theses on accountability. Administrative Theory & Praxis, doi:10.1080/10841806.2018.1512340
  • Schachter, H. L. (2007). Does Frederick Taylor’s ghost still haunt the halls of government? A look at the concept of governmental efficiency in our time. Public Administration Review, 67(5), 800–810. doi:10.1111/j.1540-6210.2007.00768.x

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