main spatial policy approaches to securing DHC through new developments in Belgium, France, Ireland, the Netherlands and the United Kingdom
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This chapter presents Critical Policy Discourse Analysis (CPDA) which merges critical discourse analysis (CDA) with critical policy studies (CPS). CPDA engages with a discursive analysis of a policy problem, generally drawing on critical discourse analysis for its methodology, in this case Text Oriented Discourse Analysis (TODA). The research addresses the problem of complexity reduction in the process of policy-making and illustrates this with an analysis of the UN Agenda “Transforming the World, the 2030 Agenda for Sustainable Development”, which introduces the sustainable development goals (SDGs). It presents the reader with a detailed example of how to perform a TODA research. It indeed reveals mechanisms of policy reduction such as decontextualization, singularization, a limited spatio-temporal frame reduced to the timespan of the UN. It discusses the potential consequences of this for the effectivity of the SDGs and presents alternative theories and voices that do capture the complexity of real life events. The final section suggests further developments in CPDA and advocates bringing complexity to the fore.
Policy analysis is a broad and versatile field of applied policy research and advice, where a multitude of perspectives and methods have developed. In this paper, we attempt to (re)structure the discipline in a single conceptual model. The model was derived on the basis of a review of relevant literature on policy analysis styles and a review of about 20 exemplary cases in the field of technology, policy and management. The model serves three purposes: understanding of policy analysis as a discipline, contribution to the design of new policy analysis methods and projects, and guidance for evaluating such methods and projects. The model identifies six activities and translates these into six underlying policy analytic styles. Each style implies different values, and calls for different criteria when it comes to evaluation. An important claim of the model is that, in practice, policy analysis consists of creatively combining these activities and styles.
MUSE supports the CIVITAS Community to increase its impact on urban mobility policy making and advance it to a higher level of knowledge, exchange, and sustainability.As the current Coordination and Support Action for the CIVITAS Initiative, MUSE primarily engages in support activities to boost the impact of CIVITAS Community activities on sustainable urban mobility policy. Its main objectives are to:- Act as a destination for knowledge developed by the CIVITAS Community over the past twenty years.- Expand and strengthen relationships between cities and stakeholders at all levels.- Support the enrichment of the wider urban mobility community by providing learning opportunities.Through these goals, the CIVITAS Initiative strives to support the mobility and transport goals of the European Commission, and in turn those in the European Green Deal.Breda University of Applied Sciences is the task leader of Task 7.3: Exploitation of the Mobility Educational Network and Task 7.4: Mobility Powered by Youth Facilitation.
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
