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Changes in the Labor cost budget within the KCDC (From 2009 to 2018). Data: Open Fiscal Data (2018) (http://www.openfiscaldata.go.kr/portal/maineng.do).

Changes in the Labor cost budget within the KCDC (From 2009 to 2018). Data: Open Fiscal Data (2018) (http://www.openfiscaldata.go.kr/portal/maineng.do).

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This paper critically reviews whether the hierarchical system or intercrisis learning can be sufficient to understand Korea's COVID-19 responses. Our case study suggests that a Korean response system is a hybrid form that uses a hierarchical structure together with a network approach. To unveil theoretical models of how learning may occur and evolv...

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... such, the incremental response has become possible based on the priorities of the government. The revised response manual also indicates that proper response recognition has gained weight in preventing severe damage during the crisis (Table 4 and Figure 5). ...

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... Additionally, an inter-departmental Homefront Crisis Executive Group chaired by the Permanent Secretary of the Ministry of Home Affairs and comprising senior representatives from all ministries was formed. Its main role was to provide the strategic and political guidance during any health crisis, such as endorsing the suitable DORSCON level (Kim et al, 2023). Compared to SARS, task forces focusing on newer aspects of disease management and control emerged during COVID-19. ...
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Ad hoc bodies such as committees, task forces and working groups are often deployed by governments on a temporary and short-term basis to respond to conditions of crisis. These groups differ from long-term advisory bodies and can help bypass typical challenges encountered in bureaucracies for governments to act quickly under crisis. Being transient in nature, formal mechanisms to track the institutional roles played by ad hoc groups under crisis are often lacking and can lead to missed opportunities for policy learning leveraging on their strengths when these are deployed again. This exploratory article applies a policy learning lens to examine experiences of five Asian economies in creating ad hoc groups during SARS and COVID-19. Recognising that learning is a complex construct, this article attempts to observe the diverse institutional roles assumed by ad hoc groups for crisis management. We position our contribution as a first step towards a better understanding of the structure and function of short-term ad hoc groups, and argue this can aid a more fruitful deployment and utilisation of similar groups for improved crisis management in the future.
... Given their complexity, and continuous evolution, creeping crises such as the COVID-19 pandemic require deliberate, and continuous policy learning processes (Lee, Hwang, and Moon 2020;Kim, Shin, and Kim 2023). There, focusing on agility is critical (Nolte and Lindenmeier 2023). ...
... Research points out the importance of identifying past cases, especially those from similar contexts, whether successful as exemplary (Lundin, € Oberg, and Josefsson 2015), or unsuccessful as cautionary (Dunlop 2017). Best practices indicate that institutionalization of such lessons provides handy resources for swift and agile crisis responses (Lee, Hwang, and Moon 2020;Kim, Shin, and Kim 2023). This is in addition to continuous scanning of emerging lessons as a crisis unfolds. ...
... Establishing clear relationships and role descriptions between advisory boards and policy learning groups across different levels of the multilevel governance architecture, both vertically and horizontally . Establishing consensus across policy learning an organizational structures regarding values, paradigms and priorities while allowing room for experimentation, and enhancing agility (Kim, Shin, and Kim 2023;Lee, Hwang, and Moon 2020). ...
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Policy learning plays a critical role in crisis policymaking. Adequate learning can lead to effective crisis responses, while misdirected learning can derail policymaking and lead to policy fiascos, potentially with devastating effects. However, creeping crises such as the recent COVID-19 pandemic pose significant challenges for doing "good" policy learning. Such crises pose persistent threats to societal values or life-sustaining systems. They evolve across time and space while stirring significant political and societal tensions. Given their inherent features, they are often insufficiently addressed by policymakers. Taking the COVID-19 crisis as an illustrative example, this article aims to draw practitioners' attention to key features of creeping crises and explains how such crises can undermine critical policy learning processes. It then discusses the need for "policy learning governance" as an approach to design, administer and manage crisis policy learning processes that are able to respond to continuous crisis evolutions. In doing so, it helps practitioners engage in adaptive and agile policy learning processes toward more effective learning by introducing four key principles of policy learning governance during creeping crises. Those are: identifying optimum learning modes and types, learning across disciplines, learning across space, and learning across time. Practical tools distilled from emerging research are then introduced to help apply the proposed principles of policy learning governance during future crises.
... Theoretically, burgeoning COVID-19 policy learning research has generated advanced theoretical understandings of how certain learning types take place and their role in creating patterns of policy change and stability (Quaglia & Verdun, 2023;Zaki, Pattyn, et al., 2022), and it also conceptualised several new learning types (Crow et al., 2022;Lee et al., 2020). Empirically and practically, this growing body of literature has shown us how good or 'optimal' policy learning contributed to successful crisis policy-making, for example through effective collective learning involving relevant stakeholders (Osei-Kojo et al., 2022), adequate engagement of expertise and cognisance of local contexts surrounding learning (Raudla, 2021), or ensuring adaptive and agile learning approaches (Kim et al., 2023;Lee et al., 2020). On the other hand, it has also shown us how subpar learning derailed crisis policy-making leading to costly failures . ...
... Our analysis shows different types of policy learning being studied. This includes social learning and instrumental learning (Zaki, Pattyn, et al., 2022), political learning (Casula & Pazos-Vidal, 2021), single-and double-loop learning (Ladi & Tsarouhas, 2020), quadruple-loop learning (Lee et al., 2020), epistemic learning (Zaki & Wayenberg, 2021), and organisational learning (Kim et al., 2023). We also find studies on both mechanisms of learning taking place. ...
... Within these learning structures, it is necessary to maximise organisational learning capabilities during crises as they progress while establishing a tolerance for experimentation (Lee et al., 2020). This means also adjusting and re-aligning organisational values, rather than simply working procedures (Kim et al., 2023). The second set is about governance structures. ...
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Policy learning plays an important role during crises, where it can empower effective crisis responses or derail policy . Accordingly, a crisis like the COVID‐19 pandemic has created a surge in research on policy learning. In this article, and more than 3 years from the crisis’ onset, we systematically review what COVID‐19 policy learning research has hitherto offered. We take stock of 45 scientific articles to provide an account of where policy learning has been researched, what methods, policy domains, and conceptual approaches were most used, and what new theoretical and conceptual advances have emerged from this growing body of research. Furthermore, we distil the key insights it offers to both scholars and practitioners. In doing so, we point to the theoretical and empirical gaps that future scholarship can address, as well as how can practitioners leverage research insights towards improving policy learning practices during similar crises in the future. Points for practitioners In creeping crises such as COVID‐19, policy‐makers need to consider the multidimensionality and societal embeddedness of policy issues while designing policy learning processes, particularly in identifying relevant expertise. Creeping crises evolve over time. Hence, policy‐makers need to continuously re‐align the policy learning processes to match evolving crisis definitions, manifestations, and societal perceptions. This requires continuous context scanning. In creeping crises, policy learning has considerable time and space interactions. Thus, when designing policy learning processes, policy‐makers need to proactively consider the heterogeneity of policy learning processes across various levels of the governance architecture over time. As such, holistic ‘governance’ of policy learning processes becomes essential. Policy‐makers should strive towards minimising perceived political interventions and influences on the policy learning processes, particularly during crises to maintain transparency and public trust.
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The predominant ontological position on agency in policy learning literature has been relatively learner-oriented, thus focusing on policy actors puzzling about policy problems. In other words, it focuses on how actors acquire, translate, and disseminate knowledge and information to address policy problems or 'puzzles'. However, despite its influence on learning and its outcomes, policy actors' powering, or agency in shaping learning processes has been scarcely explored or theorised. Drawing on policy learning literature, this article explores and demarcates the concept of 'policy learning governance' as a supplementary perspective to the learner-oriented view of agency in policy learning research. Here, learning governance can be understood as the deliberate processes by which policy actors strategise, design, and govern policy learning processes towards achieving technical or political objectives. This article explains how integrating a learning governance perspective into existing conceptual approaches to policy learning can provide a better basis for understanding the interactions between different constitutive elements of policy learning processes and outcomes, such as policy or belief change. In this way, it offers a more robust baseline for explaining learning processes, an advancement that has significant implications for both policy learning theory and practice.