Aktuelle Publikationen

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dc.contributor.author: Elff, Martin; Heisig, Jan Paul; Schaeffer, Merlin; Shikano, Susumu

We investigated the turnover intentions of employees who perceive that they are being treated with more or less abusive supervision than their coworkers. We call this incongruent abusive supervision. Our findings support our theory that employees associate incongruent abusive supervision with the anticipation of social exclusion from their coworkers. Furthermore, this appraisal of social exclusion threat is associated with feelings of shame, which, in turn, increase turnover intentions. Two experimental vignettes provide support for our theoretical model. These findings demonstrate the effect that incongruent abusive supervision has on employees’ reactions to abusive supervision and introduces shame as an emotional mechanism important for understanding employee responses to supervisor abuse both when they are singled out for abuse and when they are spared abuse while their coworkers are not.

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dc.contributor.author: Jochem, Sven

This article analyses the socio‐economic determinants of public preferences towards public spending and parental fees for childcare and how they are conditioned by institutional contexts. Previous studies of childcare policy preferences have focused on attitudes regarding the provision of care. However, when it comes to questions of financing, we know astonishingly little about how supportive individuals actually are of expanding pre‐school early childhood education and care, and how support varies across different socio‐economic groups in society. This is an important research gap because childcare provision and how it is financed have redistributive implications, which vary depending on the institutional design of childcare policy. Using novel and unique survey data on childcare preferences from eight European countries, we argue and show that preferences towards expanding childcare are more contested than it is often assumed. The institutional structure of childcare shapes how income matters for preferences towards how much should be spent and how provision should be financed. Where access to childcare is socially stratified, the poor and the rich develop different preferences towards either increasing public spending or reducing parental fees in order to improve their access to childcare. The findings in this article suggest that expanding childcare in systems characterised by unequal access can be politically contested due to diverging policy priorities of individuals from different social backgrounds.

In open-list proportional representation systems, choosing candidates based on issue proximity can improve policy congruence. However, in practice, voters may not know enough about individual candidates to do so. Hence, we examine whether voters infer individual positions from cues provided on ballots, namely age and residence. Studying the Swiss parliamentary elections of 2019, we focus on environmental policy, both a very salient issue and featuring considerable intra-party heterogeneity of positions. We combine comprehensive candidate data with a representative voter survey and conduct a survey-embedded experiment (N = 10,758). We find that citizens have indeed little knowledge of candidate positions. However, ballot cues predict policy differences among candidates within parties only to a limited extent, and the experiment does not suggest that voters use ballot information to predict positions directly. Instead, as suggested by additional analyses, citizens may perceive candidates who resemble their own sociodemographic profile as having positions closer to their own.

En este artículo revisamos distintos modelos que usan herramientas de las ciencias de la complejidad para explicar fenómenos sociopolíticos y sugerimos que estos modelos deben ser interpretados como ‘modelos mínimos’ (Weisberg 2007; Batterman y Rice 2014). Concluimos que estos modelos altamente idealizados pueden no solo ayudarnos a distinguir los factores causales relevantes que dan origen a ciertos fenómenos sociopolíticos, sino que además en algunos casos pueden ayudarnos a visualizar políticas de intervención.

Automated decision making and predictive analytics through artificial intelligence, in combination with rapid progress in technologies such as sensor technology and robotics are likely to change the way individuals, communities, governments and private actors perceive and respond to climate and ecological change. Methods based on various forms of artificial intelligence are already today being applied in a number of research fields related to climate change and environmental monitoring. Investments into applications of these technologies in agriculture, forestry and the extraction of marine resources also seem to be increasing rapidly. Despite a growing interest in, and deployment of AI-technologies in domains critical for sustainability, few have explored possible systemic risks in depth. This article offers a global overview of the progress of such technologies in sectors with high impact potential for sustainability like farming, forestry and the extraction of marine resources. We also identify possible systemic risks in these domains including a) algorithmic bias and allocative harms; b) unequal access and benefits; c) cascading failures and external disruptions, and d) trade-offs between efficiency and resilience. We explore these emerging risks, identify critical questions, and discuss the limitations of current governance mechanisms in addressing AI sustainability risks in these sectors.

Under what conditions do religious groups engage in peace activism? Religious groups engage in peace activism if the associated costs are low and they compete with either conflict party over the interpretation of their religious content concerning war and peace. Peace activism is a promising action as peace activists maintain their positive self-evaluation. I introduce a novel dataset of peace activism for 511 religious groups in 128 countries from 1990 to 2008. Conditional fixed effects and logistic models support my hypotheses. The findings highlight the role of rational considerations in the decision to conduct peace activities by representatives of religious groups.

Was tun, wenn die Feldforschung ausfällt? Eigentlich wollte der Politikwissenschaftler Fabian Bergmann für seine Doktorarbeit zu den indigenen Sámi in Norwegen und Schweden reisen. Dann kam Corona. Nun steht das Projekt vor der Schwierigkeit, Kontakte auf Distanz zu knüpfen.

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions.

Most democracies allow their members of parliament to concurrently be employed in the private sector. A widespread worry is that politicians leverage their current or past posts within parliament, for example, as ministers or committee chairs, to gain lucrative jobs. However, we know little about whether “moonlighting” income is indeed driven by these positions. I analyze comprehensive new panel data on the private sector earnings of all members of the UK House of Commons during 2010–16. Focusing on within-legislator variation, I find that currently holding an influential position does not cause an increase in income from outside jobs. Politicians do see higher earnings soon after leaving their parliamentary posts, but this effect is concentrated among cabinet ministers. The article advances the literature by identifying which political posts lead to financial benefits in the private sector—and when.