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Social participation at schoolyards is crucial for children's development. Yet, schoolyard environments contain features that can hinder children's social participation. In this paper, we empirically examine schoolyards to identify existing obstacles. Traditionally, this type of study requires huge amounts of detailed information about children in a given environment. Collecting such data is exceedingly difficult and expensive. In this study, we present a novel sensor data-driven approach for gathering this information and examining the effect of schoolyard environments on children's behaviours in light of schoolyard affordances and individual effectivities. Sensor data is collected from 150 children at two primary schools, using location trackers, proximity tags, and Multi-Motion receivers to measure locations, face-to-face contacts, and activities. Results show strong potential for this data-driven approach, as it allows collecting data from individuals and their interactions with schoolyard environments, examining the triad of physical, social, and cultural affordances in schoolyards, and identifying factors that significantly impact children's behaviours. Based on this approach, we further obtain better knowledge on the impact of these factors and identify limitations in schoolyard designs, which can inform schools, designers, and policymakers about current problems and practical solutions. [ABSTRACT FROM AUTHOR]

In this paper, the author argues that the conflict between the copious amount of digital data processed by public organisations and the need for policy-relevant insights to aid public participation constitutes a 'public information paradox'. Machine learning (ML) approaches may offer one solution to this paradox through algorithms that transparently collect and use statistical modelling to provide insights for policymakers. Such an approach is tested in this paper. The test involves applying an unsupervised machine learning approach with latent Dirichlet allocation (LDA) analysis of thousands of public comments submitted to the United States Transport Security Administration (TSA) on a 2013 proposed regulation for the use of new full body imaging scanners in airport security terminals. The analysis results in salient topic clusters that could be used by policymakers to understand large amounts of text such as in an open public comments process. The results are compared with the actual final proposed TSA rule, and the author reflects on new questions raised for transparency by the implementation of ML in open rule-making processes. [ABSTRACT FROM AUTHOR]

Policy and data scientists have paid ample attention to the amount of data being collected and the challenge for policymakers to use and utilize it. However, far less attention has been paid towards the quality and coverage of this data specifically pertaining to minority groups. The paper makes the argument that while there is seemingly more data to draw on for policymakers, the quality of the data in combination with potential known or unknown data gaps limits government's ability to create inclusive policies. In this context, the paper defines primary, secondary, and unknown data gaps that cover scenarios of knowingly or unknowingly missing data and how that is potentially compensated through alternative measures. Based on the review of the literature from various fields and a variety of examples highlighted throughout the paper, we conclude that the big data movement combined with more sophisticated methods in recent years has opened up new opportunities for government to use existing data in different ways as well as fill data gaps through innovative techniques. Focusing specifically on the representativeness of such data, however, shows that data gaps affect the economic opportunities, social mobility, and democratic participation of marginalized groups. The big data movement in policy may thus create new forms of inequality that are harder to detect and whose impact is more difficult to predict. [ABSTRACT FROM AUTHOR]

A growing body of literature focuses on how the context in which policy entrepreneurs operate shapes their actions. This study contributes to this perspective by focusing on the regional implementation of wind turbines for increasing renewable energy levels in Sweden. Sweden introduced national wind coordinators for facilitating wind energy implementation. In this capacity, the coordinators carry out entrepreneurial strategies in form of moving the policy through the administrative agenda at local level and pursuing the implementation process together with municipal stakeholders. The study shows that over time, wind coordinators were able to move beyond the government-defined activities and widen the scope of their actions. The analysis offers insights into the temporal dimension of regional entrepreneurial activities by mapping activities from 2006 to 2016. The case reveals that a flexible policy framework and more in-depth knowledge into regional struggles through mediating and networking enable the identification of potential local bottlenecks and lobbying for legal changes by entrepreneurs. [ABSTRACT FROM AUTHOR]

The buzz surrounding big data has taken shape in various theoretical and practical forms when it comes to policymaking. The paper combines current research streams with long-standing discussions on government and technology in public policy and public administration, such as e-government and evidence-based policymaking. The goal is to answer the question whether big data is a fleeting trend or has long-lasting effects on policymaking. Three larger themes in the literature are identified: First, the role that institutional capacity has within government to utilize big data analytics; second, government use of big data analytics in the context of digital public services; and finally, the way that big data information enters the policy cycle, focusing on substantive and procedural policy instruments. Examples from the education, crisis management, environmental and healthcare domain highlight the opportunities and challenges for each of these themes. Exploring the various aspects of big data and policymaking shows that big data is here to stay, but that its utilization by government will take time due to institutional barriers and capacity bottlenecks. [ABSTRACT FROM AUTHOR]

Increased pressure to use policy transfer as a tool has led policy-makers to seek it as a solution for innovation efforts. A prominent example is Silicon Valley, whose replication attempts have been widely criticised. Based on 'Silicon Somewhere' cases in Asia, Europe and the US, the research suggests that there are different types of learning contributing to the success or failure of policy transfer. These variations in the learning processes underlying the transfer include imitation, trialand- error and adaptive learning. [ABSTRACT FROM AUTHOR]