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Three doctoral students ask themselves a question: Who will take care of our parents and grandparents if they can no longer do it themselves? In Germany, Sweden and Italy there are very different answers to this question! Kristin Noack, Marlene Seiffarth, and Greta-Marleen Storath from the University of Bremen, Collaborative Research Centre Global Development Dynamics of Social Policy, tell you why, and what Poland, Ukraine, and Romania have to do with it.

Kenneth Benoit is Professor of Quantitative Social Research Methods and head of the Department of Methodology at the London School of Economics and Politics Science and part-time professor at Trinity College Dublin. His contribution to text analysis in political science should not be underestimated, where his development of computer-assisted estimation of policy positions from political texts remains a cornerstone in the literature. He maintains and created the QUANTEDA package for managing and analysing text data, and therefore is well placed to offer insights into the "big picture" of QTA, and what to expect in the future.

This workshop with Stefan Müller from Trinity College Dublin offers a hands-on introduction to extracting data from text, and applying various methods to analyse the data. Topics included involve:

• From Raw Text to Corpus – how to collect textual data and prepare it for analysis.
• Classification techniques - the first steps in translating text to usable data; supervised and unsupervised learning; dictionary approaches and topic modelling.
• Scaling – Supervised and Unsupervised techniques.
• An overview of more advanced topics.

The applied elements of the workshop will make use of the programming language R. Therefore, a basic familiarity with R is a prerequisite for attending the course.

Registration: BIGSSS fellows register via CampusNet, SOCIUM and CRC 1342 members please send a short email to mlarsen@bigsss-bremen.de

As the programme of choice for many QTA scholars, the event series begins with a brief introduction/recap to R for text analysis on November 21, 2018. This short session with Holger Döring (SOCIUM) and Nico Blocker (SOCIUM) should refresh our memories about R, and provide an overview of the useful functions particularly for Text Analysis. Therefore, we recommend attending this session even if you are a seasoned R user.

Registration: Nils Düpont, E-Mail: duepont@uni-bremen.de

Social protection is crucial for human development. However, the great majority of the global population is not or only partly covered by social protection (ILO 2014: xxi). In developing countries, in particular, it is often the very poorest and most vulnerable in a society who do not receive essential social benefits (Holliday 2000). Although the people’s need for social protection in developing countries is obvious, countries perform very differently in this respect. In some countries the reach of social protection is very limited despite high needs, while others have comprehensive social protection systems with both contributory and non-contributory components (Devereux et al. 2015). Accordingly, whereas some countries are successfully tackling poverty and inequality through a broad provision of social protection, others continue to prioritize the rights and needs of small elites over those of the broad mass of the population. This is highly problematic given that inclusive social protection is assumed to be a key factor for national productivity, global economic growth and domestic stability (Rudra 2015), and also considering that access to social protection is a human right.

How can these vast differences in the provision of social protection be explained? Elucidating the policy mix, coverage and generosity of contemporary social protection requires a deep understanding of its historical roots and specific trajectories. As most of the research continues to focus on the OECD world, however, little is known about the origins, characteristics and outcomes of social protection beyond the OECD. The narrative of welfare state emergence in rich democracies with its emphasis on domestic factors, such as industrialization and urbanization, provides only limited insight into the emergence and structure of social protection systems in other regions of the world. Several studies analyzing social protection in the Global South have emphasized that social protection has been strongly shaped by external influences and international actors over the course of the last 120 years (e.g. Gough et al. 2004; Deacon 2007; Niño‐Zarazúa et al. 2012; Midgley and Piachaud 2011; Brooks 2015; Rudra, 2015).

In order to systematically analyze alternative routes to early and contemporary social protection in low- and middle-income countries, the focus of this workshop will hence be on three of the most important external influences in the building of social protection systems in the Global South, namely colonial ties, the Cold War, as well as international donors and organizations.

In sum, this workshop brings together scholars from different disciplines with expertise in different policy areas and/or regions in order to create a larger picture of the role of external influences in the building of social protection systems in the Global South in the last 120 years. The aim is to identify alternatives routes to welfare regime building and, in consequence, to explain the differences in the policy mix, coverage, and generosity of early and contemporary social protection provision beyond the OECD.

Download: Programme

Data mining, especially as applied to social science data, is a rapidly changing and emerging field.
Data mining (DM) is the name given to a variety of computer-intensive techniques for discovering structure and for analyzing patterns in data. Using those patterns, DM can create predictive models, or classify things, or identify  different groups or clusters of cases within data. Data mining uses machine learning and predictive analytics that are already widely used in technical areas and business and are starting to spread into social science and other areas  of research. This talk will give an introduction to machine learning techniques, its challenges, applications, and pitfalls closely related to social sciences.