The author declares no conflicts of interest.

ABSTRACT Strengthening the role of evidence in policy‐making is increasingly seen as crucial for the quality and legitimacy of public policies. Both among practitioners and scholars, there is a growing awareness that evidence‐informed policy‐making not only depends on the rigor and relevance of the research, science communication or features of policy‐makers. It also depends on how expertise arrangements in public organizations are organized . Yet, we currently lack a solid understanding of how organization matters for the role of evidence in policy‐making. The article presents an organizational‐theoretical perspective on how the formal organization of public bureaucracies shapes evidence use in policy‐making, with important implications for both analysis and design of expertise arrangements in public administration. The article thereby sets a new research agenda at the intersection of organizational theory and scholarship on evidence and policy‐making, which speaks directly to public administration practice.

ABSTRACT Generating strategic foresight for public organizations is a resource‐intensive and non‐trivial effort. Strategic foresight is especially important for governments, which are increasingly confronted by complex and unpredictable challenges and wicked problems. With advances in machine learning, information systems can be integrated more creatively into the strategic foresight process. We report on an innovative pilot project conducted by an Australian state government that leveraged generative artificial intelligence (AI), specifically large language models, for strategic foresight using a design science approach. The project demonstrated AI's potential to enhance scenario generation for strategic foresight, improve data processing efficiency, and support human decision‐making. However, the study also found that it is essential to balance AI automation with human expertise for validation and oversight. These findings highlight the importance of iterative design to develop robust AI tools for strategic foresight which, alongside stakeholder engagement and process transparency, build trust and ensure practical relevance.

ABSTRACTDo no harm (DNH) represents a foundational moral framework for public administration ethics. Prohibitive moral principles, rather than aspirational values, provide a more coherent and operational basis for ethical public service—even when confronting demands for proactive intervention. Bernard Gert's theory of common morality reveals how rationality and impartiality underpin moral rules designed to prevent fundamental harms, while acknowledging challenges of defining and ranking competing harms. This approach mirrors the U.S. Constitution and Bill of Rights, which constrain governmental action to protect individual rights. We further develop the DNH framework by examining free speech issues in higher education and extend its relevance to other controversial policy settings through a comparative ethical analysis that illustrates how institutions can navigate between harmful action and harmful inaction. DNH offers morally grounded guidance for ethical decision‐making amid democratic tensions and political pressures.

ABSTRACTAs shutdowns have increased in frequency and length for the US federal government, such dysfunction may impact the federal government's ability to retain talent. In December of 2018 through January of 2019, the US federal government and its workforce endured the longest shutdown in its history. However, we know little about that shutdown's effects on the US federal civil service labor market. In this paper, we examine the intervening impact of the 2018–2019 government shutdown on personnel separations. Through the collection of data sources, we assess the shutdown's impact using two‐way fixed effects difference‐in‐differences models of both whether an agency was shut down and whether the percentage of workers who were temporarily furloughed in an agency could adequately capture the “intensity” of that shutdown. Results indicate that the 2019 shutdown impacted separations and that it did so in distinct ways across different cohorts of employees in affected agencies. This work helps inform those who study or practice strategic workforce development about the impacts of political events on labor dynamics.