Department for Computational Social Science

Abstract

Machine learning (ML) techniques have become one of the most successful scientific tools and changed the everyday life of people around the globe (e.g., search engines). A vast amount of digital data sources on human behaviour has emerged due to the rise of the internet and opened the door for computer scientists to apply ML on social phenomena. In the social sciences, however, the adoption of ML has been less enthusiastic. To investigate the relation of traditional statistics and ML, this paper shows how ML might be used as regression analysis. For that purpose, we illustrate what a typical social science approach might look like and how using ML techniques could contribute additional insights when it comes to estimators (non-linearity) or the assessment of model fit (predictive power). In particular, we reveal how epistemological differences shape the potential usage of ML in the social sciences and discuss the methodological trade-off of applying ML compared to traditional statistics.

Abstract

Wesentlicher Kern von Demokratie ist die direkte oder indirek-te Beteiligung der Bürger*innen an politischen Entscheidun-gen. In welcher Form, in welchem Umfang und in welcher In-tensität diese Beteiligung erfolgt, ist eine normative wie empi-rische Frage. Seit einiger Zeit rücken dialogorientierte Formen der Beteiligung verstärkt in den Fokus von Wissenschaft und Praxis, da mit ihnen die Hoffnung verbunden wird, aktuelle Herausforderungen in repräsentativen Demokratien besser zu bewältigen. Besonders vor dem Hintergrund dieses gestiege-nen Interesses an partizipativer und deliberativer Demokratie, ist überraschend wenig darüber bekannt, wie und in welchem Umfang solche Beteiligungsformen genutzt werden, oder in welchem Zusammenhang eine dialogorientierte partizipative Kultur mit direkt-demokratischen und repräsentativ-demokratischen Beteiligungsformen steht. Der vorliegende Beitrag greift diese Fragen auf und zeigt auf Basis von Daten auf Gemeindeebene für Baden-Württemberg, wieviel Beteili-gung, zu welchen Themen, und in welchen Formaten stattfin-det, und welche Muster in den Niveaus partizipativer, direkt-demokratischer und elektoraler Beteiligung auf Gemeinde-ebene zu finden sind.

Abstract

Mit dem Antritt der grün-roten, gefolgt von der grün-schwarzen Landesregierung in Baden-Württemberg haben dialogische Bürgerbeteiligungsformate in Baden-Württemberg an Bedeutung gewonnen. Sie werden mittlerweile auf allen politischen Ebenen genutzt und finden auch international ein breites Echo. Im Gegensatz zu repräsentativ-demokratischen Wahlen oder direkt-demokratischen Abstimmungen sind sie rechtlich kaum vorgeschrieben. In den meisten Fällen handelt es sich um freiwillig eingesetzte Formate des Austauschs zwischen (nicht organisierten) Bürger*innen, Vertreter*innen der Politik und der Verwaltung sowie gegebenenfalls weiteren zivilgesellschaftlichen oder privatwirtschaftlichen Akteur*innen. Mit ihrer Hilfe soll nicht nur die Input-, sondern auch die Output-Legitimität gestärkt werden. In diesem Band gehen verschiedene Autor*innen unterschiedlichen Fragen zu dialogischer Bürgerinnen- und Bürgerbeteiligung nach. Dabei geht es um methodische Aspekte der Erfassung und Untersuchung dieser Beteiligungsverfahren, um Sichtweisen verschiedener Akteure auf die Verfahren, ebenso wie um die Frage, wie dialogorientierte Formen der Bürgerinnen- und Bürgerbeteiligung besser mit direkt- und/oder repräsentativ-demokratischen Entscheidungsprozessen verzahnt werden können.

Abstract

Is the pursuit of interdisciplinary or innovative research beneficial or detrimental for the impact of early career researchers? We focus on young scholars as they represent an understudied population who have yet to secure a place within academia. Which effects promise higher scientific recognition (i.e., citations) is therefore crucial for the high-stakes decisions young researchers face. To capture these effects, we introduce measurements for interdisciplinarity and novelty that can be applied to a researcher’s career. In contrast to previous studies investigating research impact on the paper level, hence, our paper focuses on a career perspective (i.e., the level of authors). To consider different disciplinary cultures, we utilize a comprehensive dataset on U.S. physicists (n = 4003) and psychologists (n = 4097), who graduated between 2008 and 2012, and traced their publication records. Our results indicate that conducting interdisciplinary research as an early career researcher in physics is beneficial, while it is negatively associated with research impact in psychology. In both fields, physics and psychology, early career researchers focusing on novel combinations of existing knowledge are associated with higher future impact. Taking some risks by deviating to a certain degree from mainstream paradigms seems therefore like a rewarding strategy for young scholars.

Abstract

Applications of machine learning (ML) in industry and natural sciences yielded some of the most impactful innovations of the last decade (for instance, artificial intelligence, gene prediction or search engines) and changed the everyday-life of many people. From a methodological perspective, we can differentiate between unsupervised machine learning (UML) and supervised machine learning (SML). While SML uses labeled data as input to train algorithms in order to predict outcomes of unlabeled data, UML detects underlying patterns in unlabeled observations by exploiting the statistical properties of the data. The possibilities of ML for analyzing large datasets are slowly finding their way into the social sciences; yet, it lacks systematic introductions into the epistemologically alien subject. I present applications of some of the most common methods for SML (i.e., logistic regression) and UML (i.e., topic models). A practical example offers social scientists a ``how-to'' description for utilizing both. With regard to SML, the case is made by predicting gender of a large dataset of sociologists. The proposed approach is based on open-source data and outperforms a popular commercial application (genderize.io). Utilizing the predicted gender in topic models reveals the stark thematic differences between male and female scholars that have been widely overlooked in the literature. By applying ML, hence, the empirical results shed new light on the longstanding question of gender-specific biases in academia.

Abstract

In this paper, we investigate how protective effects of intergenerational closure correspond with conflict networks in school classes. Taking a multilevel ecological perspective, we also consider networks' socio-spatial conditions. In a first step, we use ERGMs to analyze the association between parental contact and students' friendship ties, i.e., intergenerational closure (IC). Then, we utilize spatial regressions to analyze direct and moderating effects of a school's neighborhood on conflicts in the 135 German class networks (N~=~3143 student measurements). In accordance with Coleman's theoretical notions, we find consistent negative effects of IC on the (standardized) density of the conflict networks. Moreover, we show that IC's impact is particularly strong in neighborhoods with a relatively high concentration of minorities. The results are in line with our theoretical considerations on multilevel network ecologies and the selective pressure of IC against conflict ties. Practically, our results provide evidence that fostering connections among parents (e.g., by implementing opportunity structures for parents to meet) might help to prevent deviating behavior in schools, especially in neighborhoods with relatively large proportions of ethnic minorities.

Abstract

In this chapter, Raphael Heiberger and Michael Windzio examine which topics are important for major education international organization (IOs). IOs in the field of education follow different ideological paradigms in the global education discourse. Yet, it is an open question as to whether different types of IOs focus on different topics and thereby support different paradigms of education. Based on more than 1000 documents with over 40 million words published by the World Bank, UNESCO, the ILO, the OECD, ISESCO, and SEAMEO, they explore education issues addressed in this sample. Using standardized methods of quantitative text analysis and topic modeling, Heiberger and Windzio reveal that major topics found in these documents do indeed differ between the different types of organizations.

Abstract

The European Union’s common public sphere project dates back to the 1960s and relies on Europeanisation through the gradual eradication of communication boundaries between its member countries. However, it is evident by now that Europeanisation of national public spheres is hard to achieve by increasing overlaps between national public spheres, synchronisation of news reporting across national boundaries, or diffusion of Europeanist norms into national politics. The European Union’s common public sphere project may hence be in danger. This calls for explorations of other imaginable models of the public sphere for Europe. Are there traces of other modes of transnational public sphere emerging in Europe? In this article, we explore a models of the transnational public sphere which is based on an alternative concept of Europeanisation derived from the cleavage theory. By drawing on social media data and employing tools of social network analysis, we demonstrate the empirical possibility of a cleavage model of the European public sphere.