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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.

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The Italian judicial system is well-known for its substandard performance by comparison with its European peers. In this article, we analyse the implementation of an important reform of the Italian judicial system designed and implemented in the context of the National Recovery and Resilience Plan funded by Next Generation EU. The reform, launched in February 2022, involves the introduction of a new organisational structure (The Office for the Trial) staffed with 16,500 newly appointed judicial assistants, representing an increase of about one third of the total judicial workforce employed in Italy. By leveraging the concepts of policy and organisational learning (OL), we show that variation of the modes of policy learning observed in implementation design is linked with variation in the organisational models adopted by different judicial offices to implement the reform. Then, by connecting organisational models to OL and focusing on the performance of five selected sections within the Court of Appeal of Milan, we show that organisational models informed by reflexivity are associated with better performance than models informed by hierarchy. The study contributes to scholarship on policy and OL, as well as providing a first, albeit limited and exploratory, empirical evaluation of a strategic public sector reform.

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Within the Advocacy Coalition Framework (ACF), policy-oriented learning is understood as a change in policy beliefs. Additional work has noted that belief reinforcement, not just belief change, is also a potential policy learning outcome. Yet, little work has attempted to reconcile how learning could involve both belief change and belief reinforcement. In this article, I propose a policy-oriented learning model where policy beliefs – deep core, policy core, or secondary aspects – are understood as having a distribution with a central tendency (that is, the belief) as well as variance (that is, certainty associated with the belief). With policy beliefs considered as distributions, learning can be understood as changes in beliefs (that is, a change in the central tendency) as well as changes in certainty (that is, variance), and conversely, a decrease in belief uncertainty would constitute belief reinforcement. Using data from a deliberative forum that brought together various stakeholders including experts, natural resource managers, and the public to discuss environmental issues impacting coastal communities, I explore policy-oriented learning as changes in concern regarding several key issues before and after the forum. Additionally, I examine the association between concern following the forum and self-reported learning. I find support for the proposed policy-oriented learning model as shown by significant changes in average concern as well as average variance among participants across several of the issues discussed. In this way, the article makes a theoretical contribution to the ACF literature by testing the use of distributions to assess policy learning.

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The links between policy learning and policy innovation seem self-evident. Yet these areas of scholarship have developed independently of each other. The articles in this Special Issue all address some aspect of the learning/innovation relationship. This introduction sets the scene by reviewing how innovation intersects with studies of policy learning. To do this, we explore the three key dimensions which characterise policy innovation, as defined by Sørensen, and its relationship with learning: political leadership, competition and collaboration. Viewed through a learning lens, we discern the interactions between these elements, and how they link forms of learning to innovative policy. Finally, through the lens of this learning/innovation framework, we summarise the contributions of the six articles and propose a future research agenda.

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Collaborative networks are horizontal settings of public governance that enhance interactions between a diversity of actors (for example, civil servants, companies or citizens). They can help cross-cutting public policies (for example, climate policies) to gain coherence and become more innovative. To do so, collective learning, defined as the broadened and mutual understanding of public issues arising out of repeated social interactions, is critical but not spontaneous. In particular, the diversity of participants creates learning opportunities that do not necessarily transform into concrete learning.

So, how does diversity lead to collective learning in collaborative networks? To address this research question, this article researched two collaborative networks within the city administration of Schaerbeek (Belgium). Based on semistructured interviews, mental models were used to assess collective learning, and social network analysis was performed to understand the structure of interactions between diverse members.

The findings show that the influence of diversity on collective learning was contingent on the collaborative network, but fostered by social interactions, with noticeable links between formal and informal interactions. From these findings, the article makes three scholarly contributions. First, it deepens our understanding of collective learning, with a focus on the development of shared understandings as a condition of consensus formation. Second, it builds on psychology and resource management research to assess collective learning through mental models, and provides a new approach to the measurement of policy learning. Third, it contributes to the debate on the implications of different inclusion levels and conditions for the results of collaborative governance and their transformation in policy innovations.

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Policy innovation is considered important for addressing major challenges such as climate change and the sustainable energy transition. Although policy learning is likely to play a key role in enabling policy innovation, the link between them remains unclear despite much research on both topics. To address this gap, we move beyond a binary treatment of policy innovation and differentiate policy problem innovation from policy instrument innovation and policy process innovation. Subsequently, we synthesise the literature on policy learning with the research on the multiple streams framework (MSF), a well-known lens for explaining policy innovation. Like earlier policy learning studies, we distinguish several types of learning by posing the key questions of learning, but in the context of each stream of the MSF: who learns (actors), what (beliefs), how (modes), and to what effect (ripening). This new conceptualisation clarifies the relationship of each type of policy learning to the varieties of policy innovation. Further, it indicates that policy learning is likely to result in policy innovation if and only if it influences the coupling among the three streams during a window of opportunity – through policy entrepreneurship – and not otherwise. We conclude with the implications of this study for future research on policy innovation, policy learning, and the MSF.

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Researchers and practitioners agree that collaborative innovation is crucial for problem-solving in public policy. This article contributes to our understanding of innovation in public governance, by arguing that failure in public policy is often a prerequisite for successful policy learning and innovation. Using insights from the ‘Innovator’s Dilemma’ – a theory from business studies – to develop its argument, the article emphasises how voter myopia (Nair and Howlett, 2017) plus blame avoidance by decision-makers and ill-structured policy issues, create an ‘Innovator’s Dilemma’ that produces failure before successful innovation. The article compares this theory with other concepts of innovation in the public sector and theorises different pathways to successful policy innovation. It concludes that decision-makers need to take the Innovator’s Dilemma more seriously and integrate its lessons into existing innovation models designed for the public sector.

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Ten years after Uruguay legalised cannabis, the country’s experience provides valuable insights regarding the implementation of such a policy. Uruguay’s regulation of cannabis represented an innovative policy based on a state-controlled model that yielded both expected and unexpected effects. To study these effects, we employed a qualitative research design and analysed six key outcome dimensions: accessibility, pharmacies, cannabis social clubs, quantity, prices and quality. The research included 23 interviews with key informants and 25 interviews with frequent cannabis consumers, complemented by secondary data analysis. We propose that the unintended effects of the policy, such as the formation of a ‘grey market’ – the illegal distribution of legally produced cannabis – are attributable to: (1) the novel character of the regulation and, therefore, the lack of an evidence base to guide policy design and implementation; (2) the different preferences and policy dynamics that shaped the three different governments in office during the period; and (3) an implementation gap.

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Representing and Ordering the World

Comparative practices are integral to global security politics. The balance of power politics, status competitions and global security governance would be possible without them. Yet, they are rarely treated as the main object of study.

Exploring the varied uses of comparisons, this book addresses three key questions:

• How is comparative knowledge produced?

• How does it become politically relevant?

• How do comparative practices shape security politics?

This book makes a bold, new step in uniting disparate streams of research to show how comparative practices order governance processes and modulate competitive dynamics in world politics.

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This concluding chapter summarizes and discusses the answers that the preceding chapters give to the three questions guiding the volume. Taken together, the chapters show that traditional security issues and newer ones are, for all their differences, not so dissimilar in the production of comparative knowledge, the dynamics through which it gains political relevance and the effects that comparative practices have on global security politics. The chapter particularly highlights the ambiguity of the comparative knowledge that underpins and shapes global security politics. Bringing the so far disparate research on arms dynamics, balance of power politics, status competition and knowledge production on governance objects into a common dialogue thus provides International Relations with a deeper understanding not only of the ubiquity of comparative practices but also of their fundamental role in the ordering of global security politics – and, in fact, world politics more broadly.

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