A Framework for Open Civic Design: Integrating Public Participation, Crowdsourcing, and Design Thinking

Civic problems are often too complex to solve through traditional top-down strategies. Various governments and civic initiatives have explored more community-driven strategies where citizens get involved with defining problems and innovating solutions. While certain people may feel more empowered, the public at large often does not have accessible, flexible, and meaningful ways to engage. Prior theoretical frameworks for public participation typically offer a one-size-fits-all model based on face-to-face engagement and fail to recognize the barriers faced by even the most engaged citizens. In this article, we explore a vision for open civic design where we integrate theoretical frameworks from public engagement, crowdsourcing, and design thinking to consider the role technology can play in lowering barriers to large-scale participation, scaffolding problem-solving activities, and providing flexible options that cater to individuals’ skills, availability, and interests. We describe our novel theoretical framework and analyze the key goals associated with this vision: (1) to promote inclusive and sustained participation in civics; (2) to facilitate effective management of large-scale participation; and (3) to provide a structured process for achieving effective solutions. We present case studies of existing civic design initiatives and discuss challenges, limitations, and future work related to operationalizing, implementing, and testing this framework.

Requirement Engineering Challenges for Social Sector Software Development: Insights from Multiple Case Studies

Diversity is a great challenge for software engineers in the social sector context. The objective of this paper is to contribute to the identification of the RE processes and associated challenges in releasing the software in the social sector markets for which an exploratory case study is conducted. The outcome of the case study indicates that the diversity limits the ability to involve the representative samples of user populations using the same set of RE tools and techniques as one size fits all solution for all segments. The diverse user base must be partitioned into different segments, with each segment triggered using a suitable set of RE techniques i.e., traditional and crowd-based RE. The diverse perspectives learned as a result of the interaction with each segment, must be merged together into a single perspective about the software meant to be used in the social sector. There is a need for a new RE process specially designed for handling the complexities of the social sector, which this paper terms as Social Sector Requirement Engineering (SSRE). There is an increased need for collaboration between government social sector institutions and software engineers to get access to diverse customers to improve software quality.

Public Processes are Open for Play

As public services become digital and online, both the need to design digital public services closer to citizens? expectations and to provide ways to improve their participation and engagement increases. To participate and co-design, citizens must understand how the public service is delivered, but understanding service provision is challenging for non-technical subjects. This paper argues that games can promote transparency of public service processes and citizens? understanding of them. We designed games related to three distinct public service delivery scenarios using Play Your Process (PYP), a method which translates business process models into game design artifacts. We also investigate players? ability to understand the process designed into these games through evaluation settings based on qualitative and quantitative analyses, using questionnaires and interviews. Results show that these games can help citizens understand the process execution aspects, with at least 95% confidence through statistical analysis. This research is innovative by showing an approach to systematically design serious games describing public processes as a potential tool for citizen-government transparency and understanding of public process delivery, particularly in Brazil.

Predicting the Success of Mediation Requests Using Case Properties and Textual Information for Reducing the Burden on the Court

The success of mediation is affected by many factors, such as the context of the quarrel, personality of both parties, and the negotiation skill of the mediator, which lead to uncertainty for the predicting work. This paper takes a different approach from previous legal prediction research. It analyzes and predicts whether two parties in a dispute can reach an agreement peacefully through the conciliation of mediation. With the inference result, we can know if the mediation is a more practical and time-saving method to solve the dispute. Existing works about legal case prediction mostly focus on prosecution or criminal cases. In this work, we propose a LSTM-based framework, called LSTMEnsembler, to predict mediation results by assembling multiple classifiers. Among these classifiers, some are powerful for modeling the numerical and categorical features of case information, e.g., XGBoost and LightGBM; and, some are effective for dealing with textual data, e.g., TextCNN and BERT. The proposed LSTMEnsembler aims to not only combine the effectiveness of different classifiers intelligently, but also capture temporal dependencies from previous cases to boost the performance of mediation prediction. Our experimental results show that our proposed LSTMEnsembler can achieve 85.6% for F-measure on real-world mediation data.

The Realities of Blockchain-Based New Public Governance: An Explorative Analysis of Blockchain Implementations in Europe

Blockchain technology and New Public Governance represent promising concepts for various researchers. As such, both concepts offer the vision of an altered relationship between public administration and its non-public actors by emphasizing a strong position of non-public actors for public service delivery. This research aims to identify the relevance of New Public Governance to leading blockchain implementations in the European public sector. For this purpose, both topics are combined in an explorative analysis. The analysis leverages an adapted analysis framework designed for this research effort to structure the expectations around New Public Governance. Qualitative interviews with multiple key stakeholders of blockchain implementations projects were conducted to understand the actual impact of blockchain on the actor?s relationships for public service delivery. This article presents the findings to this question and concludes that the use of blockchain has the changed actor relationships only in parts. Consequently, the author finally draws attention to the importance of blockchain governance and blockchain regulation for further developing the relationships of public administrations and their non-public counterparts.

Conceptualizing Smart Government: Interrelations and Reciprocities with Smart City

Smart government (SG) is an emerging topic, which increasingly attracts attention from scholars who work in public administration, political and information sciences. Smart city (SC) on the other hand, is an emerging and multidisciplinary domain of study. SC It is not clear whether the two terms SG and SC co-exist or concern different domains that interrelate and interact. The aim of this paper is to investigate the term SG; to conceptualize it with components; to define the importance of these components to the SG with their relative strengths; and to clarify its relationship with the SC term. In this respect, this paper follows a multi-method approach (literature review and a Delphi study), with which and the authors proposed a definition to the SG that overcomes them and while a model with 3 rings, 3 dimensions and 13 components to conceptualize it. The Delphi study showed that all the SG conceptual entities are useful, and highlighted that Citizens Engagement, Economic Growth and Accountability are more important compared to the others, but it is hard to decide about the less important component. Third, the ICT Innovation entity appears to be the most important compared to emerging technologies and data.

Network Analysis of Internal Migration in Austria

Human migration, and urbanization as its direct consequence, are among the crucial topics in regional and national governance. People?s migration and mobility flows make a network structure, with large cities acting as hubs, and smaller settlements as spokes. The essential method by which these phenomena can be analysed comprehensively is network analysis. With this study we, first, contribute to capacity building regarding the analysis of internal (national) migration data, by providing a set of network indicators, models, and visualisations tested and argued for in terms of applicability and interpretability for analysing migration. Second, we contribute to the understanding of the shape and scale of the phenomenon of internal migration, in particular towards urbanisation and mobility flows between human settlements (i.e. cities, towns and villages). Third, in the work we demonstrate the utility of our approach on the example of internal migration flows in Austria on the settlement level, and provide a longitudinal analysis for the period of 2002-2018. This is the first time that the key traits of a network of internal migration are identified for a European country, which, when accompanied by additional country analyses, has the potential to reveal the migration patterns in the region and beyond.

Citizen Participation and Machine Learning for a Better Democracy

The development of democratic systems is a crucial task as confirmed by its selection as one of the Millennium Sustainable Development Goals by the United Nations. In this article, we report on the progress of a project that aims to address barriers, one of which is information overload, to achieving effective direct citizen participation in democratic decision-making processes. The main objectives are to explore if the application of Natural Language Processing (NLP) and machine learning can improve citizens? experience of digital citizen participation platforms. Taking as a case study the ?Decide Madrid? Consul platform, which enables citizens to post proposals for policies they would like to see adopted by the city council, we used NLP and machine learning to provide new ways to (a) suggest to citizens proposals they might wish to support; (b) group citizens by interests so that they can more easily interact with each other; (c) summarise comments posted in response to proposals; (d) assist citizens in aggregating and developing proposals. Evaluation of the results confirms that NLP and machine learning have a role to play in addressing some of the barriers users of platforms such as Consul currently experience.

Hindcasting violent events in Colombia using Internet data

Colombia experienced a decades-long civil war between the government and many left-wing guerrilla groups. It was marked by violence, kidnappings, and large quantities of human displacement. Monitoring and forecasting civil wars are important to mitigate their potential impact but require access to ground truth data. We examine the use of Internet data streams, namely Google search queries, tweets related to politics, and traditional news sources to retrospectively forecast (i.e., hindcast) state-based armed violence in Colombia. We compare the results of statistical models using three combinations of these features to evaluate the predictive capabilities of each data stream. Our results show that the combination of internet and traditional news data models perform most consistently, though Internet-only is surprisingly promising. Overall, we are able to produce high-quality models hindcasting the presence or absence of state-based armed violence in Colombia up to 6 months in advance. These results support the use of exogenous data streams to forecast evolving situations around the globe.