300 Sozialwissenschaften
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This contribution presents an analysis of the structure and conflictual dynamics of contemporary German sociology which has recently separated into two professional societies. Using geometric data analysis, we present an empirical construction of the power/knowledge structure of the field, its paradigmatic plurality, and the various forms of sociological practices involved.
"Writing with my professors”
(2023)
Kollaboratives Forschen quer zu hegemonialen Wissensordnungen gilt als wichtiger Baustein dekolonialer Wissenspraxis. Gemeinsame Schreibprozesse von Wissenschaftler*innen und ihren nicht-wissenschaftlichen Forschungspartner*innen sind allerdings selten und eine methodologische und forschungspraktische Reflexion fehlt. Die Beiträger*innen widmen sich diesen Lücken, indem sie erfolgreiche, aber auch gescheiterte Projekte kollaborativer Textproduktion zwischen Universität und Feld vorstellen und auf ihr Potenzial als transformative und dekoloniale Wissenspraxis befragen. So entsteht eine praktische Orientierungshilfe, die gleichzeitig die interdisziplinäre Diskussion anregt.
State- and private-led search-and-rescue are hypothesized to foster irregular migration (and thereby migrant fatalities) by altering the decision calculus associated with the journey. We here investigate this ‘pull factor’ claim by focusing on the Central Mediterranean route, the most frequented and deadly irregular migration route towards Europe during the past decade. Based on three intervention periods—(1) state-led Mare Nostrum, (2) private-led search-and-rescue, and (3) coordinated pushbacks by the Libyan Coast Guard—which correspond to substantial changes in laws, policies, and practices of search-and-rescue in the Mediterranean, we are able to test the ‘pull factor’ claim by employing an innovative machine learning method in combination with causal inference. We employ a Bayesian structural time-series model to estimate the effects of these three intervention periods on the migration flow as measured by crossing attempts (i.e., time-series aggregate counts of arrivals, pushbacks, and deaths), adjusting for various known drivers of irregular migration. We combine multiple sources of traditional and non-traditional data to build a synthetic, predicted counterfactual flow. Results show that our predictive modeling approach accurately captures the behavior of the target time-series during the various pre-intervention periods of interest. A comparison of the observed and predicted counterfactual time-series in the post-intervention periods suggest that pushback policies did affect the migration flow, but that the search-and-rescue periods did not yield a discernible difference between the observed and the predicted counterfactual number of crossing attempts. Hence we do not find support for search-and-rescue as a driver of irregular migration. In general, this modeling approach lends itself to forecasting migration flows with the goal of answering causal queries in migration research.
The article analyzes the investigations conducted by the Berlin police into the subsequent perpetrator of the vehicle-ramming attack at a Berlin Christmas market on December 19, 2016. We explore why the police closed these investigations prematurely and thereby focus on an attempt to prevent lone actor terrorism. The analysis shows that the police closed its investigations owing to organizational dynamics driven by an increasing need to justify further resource investments in the face of absent conclusive evidence and scarce resources in relation to the organizational case ecology. We propose hypotheses for future research and formulate three contributions to existing research on the sociology of police, terrorism prevention, and lone actor research.
Taxed fairly?
(2023)
Empirically, the poor are more likely to support increases in the level of tax progressivity than the rich. Such income-stratified tax preferences can result from differences in preferences of what should be taxed as argued by previous literature. However, it may also result from income-stratified perceptions of what is taxed. This paper argues that the rich perceive higher levels of tax progressivity than the poor and that tax perceptions affect individuals’ support for progressive taxation. Using data from an Austrian survey experiment, we test this argument in three steps: First, in line with past research, we show that individuals’ income positions are connected to individuals’ tax preferences as a self-interest rationale would predict. However, second, we show that this variation is mainly driven by income-stratified tax perceptions. Third, randomly informing a subset of the sample about actual tax rates, we find that changing tax perceptions causally affects support for redistributive taxation among those who initially overestimated the level of tax progressivity. Our results indicate that tax perceptions are relevant for forming tax preferences and suggest that individuals are more polarized in their perceptions of who pays how much taxes than in their support for who should pay how much tax.
Political trust—in terms of trust in political institutions—is an important precondition for the functioning and stability of democracy. One widely studied determinant of political trust is income inequality. While the empirical finding that societies with lower levels of income inequality have higher levels of trust is well established, the exact ways in which income inequality affects political trust remain unclear. Past research has shown that individuals oftentimes have biased perceptions of inequality. Considering potentially biased inequality perceptions, I argue that individuals compare their perceptions of inequality to their preference for inequality. If they identify a gap between what they perceive and what they prefer (= fairness gap), they consider their attitudes towards inequality unrepresented. This, in turn, reduces trust in political institutions. Using three waves of the ESS and the ISSP in a cross-country perspective, I find that (1) perceiving a larger fairness gap is associated with lower levels of political trust; (2) the fairness gap mediates the link between actual inequality and political trust; and (3) disaggregating the fairness gap measure, political trust is more strongly linked to variation in inequality perceptions than to variation in inequality preferences. This indicates that inequality perceptions are an important factor shaping trust into political institutions.
In the context of persistent images of self-perpetuated technologies, we discuss the interplay of digital technologies and organisational dynamics against the backdrop of systems theory. Building on the case of an international corporation that, during an agile reorganisation, introduced an AI-based personnel management platform, we show how technical systems produce a form of algorithmic contingency that subsequently leads to the emergence of formal and informal interaction systems. Using the concept of datafication, we explain how these interactions are barriers to the self-perpetuation of data-based decision-making, making it possible to take into consideration further decision factors and complementing the output of the platform. The research was carried out within the scope of the research project ‘Organisational Implications of Digitalisation: The Development of (Post-)Bureaucratic Organisational Structures in the Context of Digital Transformation’ funded by the German Research Foundation (DFG).
Background: Following the rapid increase of asylum seekers arriving in the European Union in 2015/16, policymakers have invested heavily in improving their foresight and forecasting capabilities. A common method to elicit expert predictions are Delphi surveys. This approach has attracted concern in the literature, given the high uncertainty in experts’ predictions. However, there exists limited guidance on specific design choices for future-related Delphi surveys.
Objective: We test whether or not small adjustments to the Delphi survey can increase certainty (i.e., reduce variation) in expert predictions on immigration to the EU in 2030.
Methods: Based on a two-round Delphi survey with 178 migration experts, we compare variation and subjective confidence in expert predictions and assess whether additional context information (type of migration flow, sociopolitical context) promotes convergence among experts (i.e., less variation) and confidence in their own estimates.
Results: We find that additional context information does not reduce variation and does not increase confidence in expert predictions on migration.
Conclusions: The results reaffirm recent concerns regarding the limited scope for reducing uncertainty by manipulating the survey setup. Persistent uncertainty may be a result of the complexity of migration processes and limited agreement among migration experts regarding key drivers.
Contribution: We caution policymakers and academics on the use of Delphi surveys for eliciting expert predictions on immigration, even when conducted based on a large pool of experts and using specific scenarios. The potential of alternative approaches such as prediction markets should be further explored.
A circulatory loop
(2023)
In the digitalization debate, gender biases in digital technologies play a significant role because of their potential for social exclusion and inequality. It is therefore remarkable that organizations as drivers of digitalization and as places for social integration have been widely overlooked so far. Simultaneously, gender biases and digitalization have structurally immanent connections to organizations. Therefore, a look at the reciprocal relationship between organizations, digitalization, and gender is needed. The article provides answers to the question of whether and how organizations (re)produce, reinforce, or diminish gender‐specific inequalities during their digital transformations. On the one hand, gender inequalities emerge when organizations use post‐bureaucratic concepts through digitalization. On the other hand, gender inequalities are reproduced when organizations either program or implement digital technologies and fail to establish control structures that prevent gender biases. This article shows that digitalization can act as a catalyst for inequality‐producing mechanisms, but also has the potential to mitigate inequalities. We argue that organizations must be considered when discussing the potential of exclusion through digitalization.