9607 research outputs found
Sort by
AmendMe: A Tabletop Game to Teach the European Parliament's Legislative Process
No full textThe European Parliament Role-Play Game (EP RPG) is a multiplayer simulation in which the players take on the role of Members of the European Parliament (MEP). The game aims to help young European citizens understand the EU law-making process and to increase their interest in politics. It exists in a physical version, available to play on-site in Brussels and in many other capital cities all over Europe, as well as a virtual version that can be played in any suitably sized location, provided there are at least 16 players and a stable Internet connection. While the existing versions of the EP RPG strive to reach different audiences, they may still not be equally accessible to all players. For example, playing the physical game requires advance planning and travel, which may be a challenge for schools in rural and remote areas. Meanwhile, the virtual game may be difficult to play at schools with an unstable internet connection or limited hardware resources, or by smaller groups who would like to play outside of class. This paper focuses on developing a tabletop version of the EP RPG optimised for accessibility to a broader range of players. The game design focuses on game elements that support discussion among players and has them reenact key aspects of the EU legislative process, such as decision-making, compromise, and voting. Feedback from the pilot playtesting was encouraging and confirmed that the game was engaging and educational, but revealed that its content and mechanics require further adjustment
Interorganizational preparedness in business-to-business relationships
No full textIn recent years, business-to-business firms have experienced increasing uncertainty, disruptive events, and major crises that have challenged their businesses. While these developments have triggered a focus on firms' preparedness to handle uncertainty, surprisingly little has been said about preparedness in an interorganizational context. This oversight is noteworthy, as interorganizational contexts are not only the dominant settings within business markets but also key drivers of the development of resilience and responsiveness. This conceptual paper outlines the concept of preparedness in business-to-business relationships and suggests a research agenda for interorganizational preparedness—an important concept in a fast-changing and uncertain business environment
Dream content discovery from social media using natural language processing
Get PDFDreaming is a fundamental but not fully understood part of human experience. Traditional dream content analysis practices, while popular and aided by over 130 unique scales and rating systems, have limitations. Often based on retrospective surveys or lab studies, and sometimes on in-home dream reports collected over some days, they struggle to be applied on a large scale or to show the importance and connections between different dream themes. To overcome these issues, we conducted data-driven mixed-method analysis identifying topics in free-form dream reports through natural language processing. We applied this analysis on 44,213 dream reports from Reddit’s r/Dreams subreddit, where we uncovered 217 topics, grouped into 22 larger themes: the most extensive collection of dream topics to date. We validated our topics by comparing it to the widely-used Hall and van de Castle scale. Going beyond traditional scales, our method can find unique patterns in different dream types (like nightmares or recurring dreams), understand topic importance and connections (like finding a greater predominance of indoor location settings in Reddit dreams than what was in general stipulated by previous work), and observe changes in collective dream experiences over time and around major events (like the COVID-19 pandemic and the recent Russo-Ukrainian war). We envision that the applications of our method will provide valuable insights into the complex nature of dreaming and its interplay with our waking experiences
In the Relational Sandbox: Deep Democracy and Technology
Get PDFDesigning for democracy often emphasizes values while overlooking the role of relationships in shaping civic life. This workshop explores how relationality – encompassing social ties, political agency, and economic conditions – shapesgrassroots democratic experiments and the technologies they inspire. Grounded in principles of deep democracy and participatory approaches, we use the Relational Sandbox to conduct small- scale experiments on how democratic technologies can redistribute power and how locally rooted designs can resist extractive technology development. Through the lens of relational civics, we co-imagine strategies prioritizing meaningful, democratic technologies over capital-driven ones. The workshop invites designers, activists, and researchers to co-develop strategies and design guidelines that place relationships at the center ofdemocratic practice
Reminiscences on Influential Papers
No full textThis issue's contributions highlight the impact and educational value of the qualitative and quantitative analysis papers. Enjoy reading! While I will keep inviting members of the data management community, and neighboring communities, to contribute to this column, I also welcome unsolicited contributions. Please contact me if you are interested
Body Politics: Unpacking Tensions and Future Perspectives for Body-Centric Design Research in HCI
Get PDFHuman bodies are deeply political as they carry historical and social meanings, including race, gender, sexuality, ethnicity, class, and abilities. The expanding body-centric research in HCI can be traced in the plurality of methods, theories and domains that take bodies as a central point of departure, when designing or studying interaction with technologies. This one-day workshop will bring together researchers and practitioners within the CHI community to discuss, map, and unpack emerging tensions and challenges on the topic of body politics for HCI. Interested participants are invited to submit examples from their own research, which, in the workshop, will be used as a point of departure to critically reflect on and expand body-centric methods, theories and domains through the lens of body politics. Workshop outcomes will include charting future directions for body-centric research to address challenges and opportunities of acknowledging that bodies are always political in design research
Cohesive urban bicycle infrastructure design through optimal transport routing in multilayer networks
Get PDFBicycle infrastructure networks must meet the needs of cyclists to position cycling as a viable transportation choice in cities. In particular, protected infrastructure should be planned cohesively for the whole city and spacious enough to accommodate all cyclists safely and prevent cyclist congestion—a common problem in cycling cities like Copenhagen. Here, we devise an adaptive method for optimal bicycle network design and for evaluating congestion criticalities on bicycle paths. The method goes beyond static network measures, using computationally efficient adaptation rules inspired by optimal transport on the dynamically updating multilayer network of roads and protected bicycle lanes. Street capacities and cyclist flows reciprocally control each other to optimally accommodate cyclists on streets with one control parameter that dictates the preference of bicycle infrastructure over roads. Applying our method to Copenhagen confirms that the city’s bicycle network is generally well-developed. However, we are able to identify the network’s bottlenecks, and we find, at a finer scale, disparities in network accessibility and criticalities between different neighbourhoods. Our model and results are generalizable beyond this particular case study to serve as a scalable and versatile tool for aiding urban planners in designing cycling-friendly cities
Testing and Symbolic Analysis For Reinforcement Learning
Get PDFReinforcement learning (RL) is a type of active learning whereby an agent learns to act in an environment by interacting with it. RL has applications in many domains, including robotics, gaming, electronics, healthcare, water management systems, etc. The majority of real-world applications of RL, such as those in robotics, necessitate a preliminary training phase in a simulation environment. It is otherwise either infeasible or prohibitively expensive to train the agent in a real-world setting. At the same time, there are clear advantages to be gained from the use of formal methods for the enhancement of software qualities. Given that RL and its applications are computer programs, the objective of this thesis is to employ formal methods, in particular pecification, testing, and symbolic execution, in order to improve the reliability and explainability of reinforcement learning
Scientification through privatization:POL-INTEL in Denmark
No full textBased on the empirical case of the POL-INTEL platform used by the Danish police, which is a customized version of Palantir Technologies’ Gotham platform, this article traces the interrelation between scientification and policing in the digital era as articulated in and through a private actor. Ethnographic fieldwork, interviews, publicly available policy documents as well as documents detailing police practices, organization, ambitions and workflows are used to problematize digital policing platforms and practices in terms of wider criminological theories and models. We show that POL-INTEL epitomizes a historical trajectory of scientification through privatization by drawing together intelligence practices, market logics, and datafication methodologies. With this point of departure, we trace back how the entanglement of private actors and the police organization raises concerns about the black boxing of criminological procedures, and the delegation of decisions and knowledge within the criminal justice system to private actors lacking public values.<br/
External Validation of an Algorithm to Guide Opioid Administration at the End of Surgery—Protocol for an Observational Cohort Study of the OPIAID Algorithm
No full textBackground:Despite advances in pain management, inadequate pain relief and opioid-related adverse events remain common challenges in perioperative care, often contributing to prolonged recovery and reduced quality of life. The perioperative opioid algorithms for individualized dosing (OPIAID) project aims to develop machine-learning algorithms tailored to provide patient-specific opioid dosing across the different phases of perioperative care. For each phase, eight models are trained on granular data from 1.1 million surgical procedures, including demographic and surgical details, vital signs, administered analgesics, pain, and opioid-related adverse events. The two most accurate models will proceed to external validation. The best-performing model will subsequently be tested as a decision support against current standard of care.ObjectivesThis protocol describes the design and external validation of the intraoperative OPIAID algorithm, which suggests the end-of-surgery opioid dose intended for postoperative analgesia by approximating clinical performance and evaluating reliability, agreement, and calibration.Methods:In this multicenter, TRIPOD+AI-adherent, prospective observational cohort study, we will collect data from a diverse surgical population of 656 adult patients undergoing elective or acute surgery under general anesthesia. All patients will require intraoperative opioid administration at the end of surgery for postoperative pain management and a subsequent stay in the post-anesthesia care unit. The cohort will be used to externally validate two machine-learning models through standardized measures of reliability, agreement, and calibration, and thereby designate the intraoperative OPIAID algorithm. Subsequently, the cohort will be used to approximate the clinical efficacy, safety and overall performance of the intraoperative OPIAID algorithm's recommended doses versus the clinician-administered doses. These comparisons will be based on each approach's proximity to a golden standard “optimal dose,” which is calculated based on a predefined generic ruleset incorporating intraoperative opioid dosing, postoperative pain, opioid-related adverse events, and need for rescue opioid administrations.Conclusion:The intraoperative OPIAID algorithm is intended as a clinical decision aid for anesthesiologists and nurse anesthetists in providing adequate postoperative pain management