Winners of the Open ODISSEI eScience Call 2021 announced
3 Nov 2021 - 6 min
The Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) and the Netherlands eScience Center are pleased to announce five winning proposals for the joint annual Open ODISSEI eScience Call 2021. This call supports researchers whose main area of expertise is in the Social Sciences.
Each of the winning projects receives a grant consisting of three months of in-kind support by Research Software Engineers from the eScience Center.
The winning projects are:
Interacting factors in school choice: modelling consequences for school segregation with an agent-based model
Andreas Flache (University of Groningen)
School segregation, the (uneven) distribution of children with particular characteristics (e.g., ethnicity, income, education) between schools, is a persistent problem in many educational systems, while schools are also considered to be important for decreasing inequalities and promoting integration. Research identified various factors influencing school segregation, but mostly looked at households as isolated individuals (choice analysis, interviews) or at macro-level statistics (trends, levels). Interactions between and within individuals and schools are often not accounted for, but it is precisely these interactions that make school choice a complex system. It could lead to an incorrect understanding of the problem if the complexity of interactions is not modelled as such. Therefore, this project aims to model interactions explicitly in an Agent-Based Model (ABM) incorporating empirical data on multiple levels (e.g., households, schools, institutions) to study the consequences of interdependent, yet uncoordinated individual choices for the emergence of school segregation.
Markets for resilience or “disaster capitalism”? A multi-method critical discourse analysis of OECD’s “Studies on Water” reports (2009-2021)
Jurian Edelenbos (Erasmus University Rotterdam)
The Organisation for Economic Co-operation and Development (OECD) is a major donor and norm-setter in global water governance. Its research programme, “Studies on Water,” has produced 53 long policy reports since 2009. We will identify OECD’s framings around the role for trans-national private sector in the Global South’s water governance. First, we will analyse how OECD manages tensions between advocacy for active private sector participation (PSP) and well-established evidence of fraught outcomes of PSP in practice. Second, we will study how OECD manages tensions between an apparent self-interest in promoting the OECD-based private sector and advocating for good water governance. Through innovative integration of qualitative critical discourse analysis and quantitative topic modelling, we identify key topics and actors, their networks, vocabulary choices, and inter-textuality within the dataset of OECD documents giving key critical insight to those tensions. A comparative examination with 12 UN-Water publications (2003 – 2021) offers further insights on OECD’s discourses.
The robot or the brain? Building a classifier for visual news frames of Artificial Intelligence
Irina Lock (University of Amsterdam)
Artificial intelligence (AI) applications, such as virtual assistants, are often portrayed as brains or human-like robots in media images. News media influence how readers think about these new, rapidly evolving technologies by putting them in a specific light through visual framing. Images grasp readers’ attention, imprint in their memory, and speak to their emotions. Thus, the way news media visually frame AI matters for how people think and talk about it, and whether they may ultimately accept these advanced technologies. Therefore, this project aims to describe the frames news media use to visualize AI, also across cultures. To do so, it will develop a custom trained news image classifier trained and validated on public online image databases. Finally, the news image classifier will analyse AI news images across the Netherlands, United States, and United Kingdom to describe the visual framing of AI and explore differences.
Tracking and visualizing states as fossil-fuel owners
Javier Garcia-Bernardo (University of Amsterdam)
Nation-states still own a control about 60 per cent of global oil and gas production. This makes states as owners an important actor for global decarbonization efforts. At the same time, we have little systematic knowledge about where exactly states are invested in fossil-fuel companies, especially cross-border. Using the largest dataset on fossil fuel state ownership, this project seeks to understand how strong states are invested in fossil-fuelled companies around the world. To do so, we will create an open-source visualization and tracking tool of (cross-border) flows and apply it in the context of fossil fuel investment. This tool will allow researchers, journalists, and the general public to better understand the role of states in the ongoing green transformation, and to direct decarbonization demands more precisely and effectively as state actors.
Transparency in the Netherlands’ non-profit sector
René Bekkers (Vrije Universiteit Amsterdam)
The contribution of philanthropic activities to positive societal change remains unclear due to a lack of data at the level of philanthropic organizations. This project will use computational social science tools to enhance research on the transparency and insight in the activities and outcomes of philanthropic organizations in the Netherlands. We will describe the contributions of philanthropic organizations to the Sustainable Development Goals, the relation between philanthropic activities, social cohesion, and inequality, the network of relationships among philanthropic organizations, and their relationships with networks of corporations and nobility. To do so, we will provide a publicly available database on the activities of audited and approved philanthropic organizations, including data on contributions to Sustainable Development Goals, financial data, and organizational identifiers that allow to link with other datasets.
Congratulation to all the winners, and a big thank you to all participants for their interesting submissions. We look forward to continue our work together with researchers in the ODISSEI community.