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Background and purposeWithin Northwest European Welfare states, there is a growing need for all social work professions to substantiate their work with research. The earliest notions of social street work origins from the end of the18th century by the British Salavation Army (Mikkonen et al., 2007). In the Netherlands it’s introduced from the United States (1960s), as a response to individuals and groups hanging around. Social street work is a low threshold and professional form of being there, performed in surroundings and situations where the target group is. It focusses on contact-making and staying in contact with individuals and marginalised groups, who otherwise are hard to reach, have lost their connection with society and have multiple problems. It’s a high appreciated practice, but it lacks a method that is substantiated with research (Morse et all, 1996; Kirkpatrick, 2000). In this paper we will present conceptual model of the method of social street work, that’s substantiated with experiences from professionals and the target group.MethodThis paper is based on a combination of literature review, document analysis, Delphi Method and an online questionnaire among the target group. The research is conducted at Streetcornerwork in Amsterdam. Streetcornerwork is the only organization in the Netherlands that provides social street work, since WWII. They employ 175 professional social street workers and has 43 years of experience in social street work.First, a theoretical model of social street work is developed bases on literature review, analyses of documents of the establishment (1970-1990)of social street work (Netherlands) and different attempts to describe the method (1991-2017). Second, the explanation model is strengthened with data from the online questionnaire among 1600 clients of Streetcornerwork. Third, the Delphi Method is used to validate the model with the tacit knowledge of 24 professionals.ResultsThe result is a conceptual model of the method of social street work that is substantiated with experiences from professionals and the target group. Characteristic is that it’s an open approach in contact with the target group which is highly dependent on context and has unpredictable character (Metz, 2016 , Andersson, 2011).The method social street work consists of 14 methodic principles,. Social street work contributesto the development of self-insight and general life skills, the restoration of the social network and the improvement of living conditions and the well-being of the target group. We also gain insight in the experienced contribution of social street work from persons in the environment of the target group (client system, neighborhood and institutional environment). This experienced contribution of social street work at theenvironment is divided into the direct contribution and the implicated contribution through the target group.Conclusions and implicationsThis conceptual model of the method of social street work contributes to a body of knowledge. We made tacit knowledge explicit and we can legitimize the profession of social street work. Because research is done in close collaboration with street workers, it also contributes to the development of their work.
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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.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
This project develops a European network for transdisciplinary innovation in artistic engagement as a catalyst for societal transformation, focusing on immersive art. It responds to the professionals in the field’s call for research into immersive art’s unique capacity to ‘move’ people through its multisensory, technosocial qualities towards collective change. The project brings together experts leading state-of-the-art research and practice in related fields with an aim to develop trajectories for artistic, methodological, and conceptual innovation for societal transformation. The nascent field of immersive art, including its potential impact on society, has been identified as a priority research area on all local-to-EU levels, but often suffers from the common (mis)perception as being technological spectacle prioritising entertainment values. Many practitioners create immersive art to enable novel forms of creative engagement to address societal issues and enact change, but have difficulty gaining recognition and support for this endeavour. A critical challenge is the lack of knowledge about how their predominantly sensuous and aesthetic experience actually lead to collective change, which remains unrecognised in the current systems of impact evaluation predicated on quantitative analysis. Recent psychological insights on awe as a profoundly transformative emotion signals a possibility to address this challenge, offering a new way to make sense of the transformational effect of directly interacting with such affective qualities of immersive art. In parallel, there is a renewed interest in the practice of cultural mediation, which brings together different stakeholders to facilitate negotiation towards collective change in diverse domains of civic life, often through creative engagements. Our project forms strategic grounds for transdisciplinary research at the intersection between these two developments. We bring together experts in immersive art, psychology, cultural mediation, digital humanities, and design across Europe to explore: How can awe-experiences be enacted in immersive art and be extended towards societal transformation?
