Papers

Cities have become central actors in migration governance, not only as implementers of national policies but also as sites of innovation and collaboration. While research has highlighted cities’ growing involvement in migration and integration, less attention has been paid to the processes through which they acquire the knowledge and capacity to shape these policies. This article addresses this gap by examining policy learning as a key mechanism through which cities engage with migration challenges and opportunities. To explore this, the study focuses on two cities – Fuenlabrada (Spain) and Ghent (Belgium) – that have shown strong engagement in migration matters and promoted inclusive approaches. The research adopts a qualitative, comparative case study design, combining document analysis with semi-structured interviews with local policymakers, civil society actors, and other stakeholders. This methodology makes it possible to trace how knowledge is acquired, exchanged, and mobilised in policymaking, as well as how contextual factors influence learning dynamics. By examining how different forms of learning emerge and how political, institutional, and social contexts shape them, the article seeks to shed light on the mechanisms that enable cities to adapt, build capacity, and respond to migration challenges. The study aims to contribute to migration scholarship by shifting attention from policy outcomes to the learning processes that underpin them, and to broader debates on urban governance by highlighting the ways in which local governments acquire and utilise knowledge in addressing complex and contested policy issues.

Why do citizens continue to protest despite high costs and low chances of success? Existing research has emphasised instrumental and expressive logics to explain this paradox, but has paid far less attention to the role of moral motivations. This paper investigates the extent to which a duty to protest drives individual engagement, arguing that this moral attitude operates both as a stable disposition and a contextually activated norm. Drawing on three recent waves of the Spanish Political Attitudes (POLAT) panel survey, I examine whether individuals driven by a strong sense of duty are more likely to engage in protest across different domains. Results demonstrate that the duty to protest consistently predicts participation across protest types, with stronger between-person differences than within-person variation. Specifically, individuals with higher average levels of duty are substantially more likely to protest, even after accounting for conventional predictors such as political interest, union membership, and ideology. These findings provide the first longitudinal evidence in political science that duty independently motivates participation across diverse forms of protest action. The study advances theoretical debates by showing that protest is not merely strategic or expressive, but also morally driven, and calls for greater attention to civic duty in explanations of contentious action.

Do extremists exhibit similar patterns of political information-seeking online? In recent years, social media and the Internet have been blamed for spreading toxic and extremist discourse. However, we still know very little about the pathways and types of information sources accessed by individuals who self-identify as extreme leftists or rightists. In this paper, we examine the information consumption habits of individuals with different ideological leanings. Using panel survey data and web-tracking data from 893 individuals in Spain, collected one month before and after the 2023 General Election, we analyze all political content individuals were exposed to in online news outlets and YouTube channels using Large Language Models (LLMs). Next, by implementing Markov chains and Association Rules, we sequentially trace all information pathways among far-right, far-left, and the remainder of the population. Preliminary results indicate that far-right individuals consume more political content than the average respondent, accessing content from legacy media, extremist websites, and YouTube political videos. They also differ from far-left individuals and non-extremists in the pathways and social media referrals they use to access those information sources, tending to remain in extremist sources and moving to them just after having read Legacy Outlets, which they do  more often than other individuals. These findings reveal distinct pathways and information diets among individuals, highlighting ideological differences and the role of social media and the Internet in shaping polarized political habits.

Official statistics often miss how much regulation really costs. In Germany’s OnDEA database, any rule with estimated annual compliance costs below €1 million is recorded as zero, effectively erasing a large number of small but meaningful obligations. Previous research on regulatory burdens has focused on aggregate totals and rule counts but has largely ignored how data conventions, such as cost censoring, systematically hide the everyday burdens of compliance. This omission leaves a major blind spot in the study of regulatory governance and the politics of measurement. This paper addresses that gap by reconstructing the missing costs for businesses, public administrations, and citizens using a model explicitly designed to handle censored data. Combining random forests with an inverse hyperbolic sine (IHS) transformation, the approach estimates plausible values for omitted entries while preserving the overall structure of the dataset. The findings show that regulations once recorded as costless in official reporting actually impose substantial hidden burdens across society. Most arise from one-time administrative or technical requirements such as registrations, certifications, and IT system upgrades. While some of these measures implement EU directives, the majority are rooted in national legislation. Together, they reveal how data conventions and measurement practices can obscure the everyday impact of regulation. By integrating modern machine-learning imputation with administrative cost accounting, this study provides both a methodological and empirical contribution. It offers a replicable framework for recovering hidden compliance costs. It also highlights how decisions about data architecture and measurement shape our understanding of the state’s regulatory footprint and, ultimately, define what counts as burden in governance.