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Strength of weakness
(2020)
The paper investigates quality management in teaching and learning in higher education institutions from a principal-agent perspective. Based on data gained from semi-structured interviews and from a nation-wide survey with quality managers of German higher education institutions, the study shows how quality managers position themselves in relation to their perception of the interests of other actors in higher education institutions. The paper describes the various interests and discusses the main implications of this constellation of actors. It argues that quality managers, although they may be considered as rather weak actors within the higher education institution, may be characterised as having a strength of weakness due to diverging interests of their principals.
Media discourse about Islamist terrorism can be understood as an important source for the construction of meaning and reality. This chapter aims to explore the different meanings of threat constituted by the media discourse about Islamist terrorism. Additionally, it seeks to shed light on the role of anti-Muslim stereotypes and racism in the discursive construction of meaning and knowledge. Therefore, this study examines the discourse on three terrorist events from the years 2015 and 2016 gathered from four major German newspapers. By applying the Sociology of Knowledge Approach to Discourse (SKAD), the findings reveal three interpretive schemes about threats associated with Islamist terrorism and their different references to anti-Muslim stereotypes and racism.
Using novel longitudinal data, this paper studies the short- and medium-term effects of Russia's invasion of Ukraine on February 24, 2022 on social trust of adolescents in Germany. Comparing adolescents who responded to our survey shortly before the start of the war with those who responded shortly after the conflict began and applying difference-in-differences (DiD) models over time, we find a significant decline in the outcome after the war started. These findings provide new evidence on how armed conflicts influence social trust and well-being among young people in a country not directly involved in the war.
Residential segregation is a wide-spread phenomenon that can be observed in almost every major city.
In these urban areas residents with different racial or socioeconomic background tend to form homogeneous clusters.
Schelling's famous agent-based model for residential segregation explains how such clusters can form even if all agents are tolerant, i.e., if they agree to live in mixed neighborhoods.
For segregation to occur, all it needs is a slight bias towards agents preferring similar neighbors.
Very recently, Schelling's model has been investigated from a game-theoretic point of view with selfish agents that strategically select their residential location.
In these games, agents can improve on their current location by performing a location swap with another agent who is willing to swap.
We significantly deepen these investigations by studying the influence of the underlying topology modeling the residential area on the existence of equilibria, the Price of Anarchy and on the dynamic properties of the resulting strategic multi-agent system. Moreover, as a new conceptual contribution, we also consider the influence of locality, i.e., if the location swaps are restricted to swaps of neighboring agents.
We give improved almost tight bounds on the Price of Anarchy for arbitrary underlying graphs and we present (almost) tight bounds for regular graphs, paths and cycles. Moreover, we give almost tight bounds for grids, which are commonly used in empirical studies.
For grids we also show that locality has a severe impact on the game dynamics.
German and European migration policy operates in permanent crisis mode. Sudden increases in irregular immigration create a sense of loss of control, which is instrumentalised by populist forces. This has generated great interest in quantitative migration predictions. High expectations are placed in the AI-based tools currently under devel­op­ment for forecasting irregular migration. The potential applications of these tools are manifold. They range from managing and strengthening the EU's reception capacity and border protections to configuring humanitarian aid provision and longer-term planning of development programmes. There is a significant gap between the expectations placed in the new instruments and their practical utility. Technical limits exist, medium-term forecasts are methodologically implausible, and channels for feeding the results into political decision-making processes are lacking. The great demand for predictions is driven by the political functions of migration prediction, which include its uses in political communication, funding acquisition and legitimisation of political decisions. Investment in the quality of the underlying data will be more productive than developing a succession of new prediction tools. Funding for applications in emergency relief and development cooperation should be prioritised. Crisis early warning and risk analysis should also be strengthened and their networking improved.
In 2022, there were 4.62 billion social media users worldwide. Social media generates a wealth of data which migration scholars have recently started to explore in pursuit of a variety of methodological and thematic research questions. Scholars use social media data to estimate migration stocks, forecast migration flows, or recruit migrants for targeted online surveys. Social media has also been used to understand how migrants get information about their planned journeys and destination countries, how they organize and mobilize online, how migration issues are politicized online, and how migrants integrate culturally into destination countries by sharing common interests. While social media data drives innovative research, it also poses severe challenges regarding data privacy, data protection, and methodological questions relating to external validity. In this chapter, I briefly introduce various strands of migration research using social media data and discuss the advantages, disadvantages, and opportunities.
In 2015, German Chancellor Angela Merkel decided to allow over a million asylum seekers to cross the border into Germany. One key concern was that her decision would signal an open-door policy to aspiring migrants worldwide – thus further increasing migration to Germany and making the country permanently more attractive to irregular and humanitarian migrants. This ‘pull-effect’ hypothesis has been a mainstay of policy discussions ever since. With the continued global rise in forced displacement, not appearing welcoming to migrants has become a guiding principle for the asylum policy of many large receiving countries. In this article, we exploit the unique case study that Merkel's 2015 decision provides for answering the fundamental question of whether welcoming migration policies have sustained effects on migration towards destination countries. We analyze an extensive range of data on migration inflows, migration aspirations and online search interest between 2000 and 2020. The results reject the ‘pull effect’ hypothesis while reaffirming states’ capacity to adapt to changing contexts and regulate migration.
Background
Many high-income countries are grappling with severe labour shortages in the healthcare sector. Refugees and recent migrants present a potential pool for staff recruitment due to their higher unemployment rates, younger age, and lower average educational attainment compared to the host society's labour force. Despite this, refugees and recent migrants, often possessing limited language skills in the destination country, are frequently excluded from traditional recruitment campaigns conducted solely in the host country’s language. Even those with intermediate language skills may feel excluded, as destination-country language advertisements are perceived as targeting only native speakers. This study experimentally assesses the effectiveness of a recruitment campaign for nursing positions in a German care facility, specifically targeting Arabic and Ukrainian speakers through Facebook advertisements.
Methods
We employ an experimental design (AB test) approximating a randomized controlled trial, utilizing Facebook as the delivery platform. We compare job advertisements for nursing positions in the native languages of Arabic and Ukrainian speakers (treatment) with the same advertisements displayed in German (control) for the same target group in the context of a real recruitment campaign for nursing jobs in Berlin, Germany. Our evaluation includes comparing link click rates, visits to the recruitment website, initiated applications, and completed applications, along with the unit cost of these indicators. We assess statistical significance in group differences using the Chi-squared test.
Results
We find that recruitment efforts in the origin language were 5.6 times (Arabic speakers) and 1.9 times (Ukrainian speakers) more effective in initiating nursing job applications compared to the standard model of German-only advertisements among recent migrants and refugees. Overall, targeting refugees and recent migrants was 2.4 (Ukrainians) and 10.8 (Arabic) times cheaper than targeting the reference group of German speakers indicating higher interest among these groups.
Conclusions
The results underscore the substantial benefits for employers in utilizing targeted recruitment via social media aimed at foreign-language communities within the country. This strategy, which is low-cost and low effort compared to recruiting abroad or investing in digitalization, has the potential for broad applicability in numerous high-income countries with sizable migrant communities. Increased employment rates among underemployed refugee and migrant communities, in turn, contribute to reducing poverty, social exclusion, public expenditure, and foster greater acceptance of newcomers within the receiving society.