How do voters in consolidating democracies see electoral integrity? How does election affect the change in perception of electoral integrity among these voters? What role does winning play in seeing an election as free and fair? Building on the theory of the winner-loser gap, we answer these questions using original two-wave panel surveys we conducted before and after three parliamentary elections in Southeast Europe in 2018 and 2020. The article focuses on changes of perception of electoral integrity as a function of satisfaction with the electoral results in contexts where the quality of elections has always been at the centre of political conflict. We specifically explore the socialization effect of elections in environments with notoriously low trust in political institutions and high electoral stakes. The article goes beyond the "sore loser" hypothesis and examines voters' both political preferences and personal characteristics potentially responsible for the change in perception of electoral integrity over the course of electoral cycle.

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Efforts to combat the COVID-19 crisis were characterized by a difficult trade-off: the stringency of the lockdowns decreased the spread of the virus, but amplified the damage to the economy. In this study, we analyze public attitude toward this trade-off using a survey-embedded experiment conducted with a quota sample of more than 7,000 respondents from Southeast Europe, collected in April and May 2020. The results show that public opinion generally favored saving lives even at a steep economic cost. However, the willingness to trade lives for the economy was greater when the heterogeneous health and economic consequences of lockdown policies for the young and the elderly were emphasized. Free-market views also make people more accepting of higher casualties, as do fears that the instituted measures will lead to a permanent expansion of government control over society.

This paper addresses the influence of organized crime on the performance of democracy in the Czech Republic and seeks to determine which dimensions of its political system (if any) are most endangered. We construe organized crime in terms of corruption networks, questioning in effect the predominant understanding of these two concepts as distinct or even exclusive phenomena. The paper thus construes corruption and organized crime as concepts referring to transgressive acts (i.e., behavior that involves a violation of moral or social boundaries that need not be legally codified), rather than in terms of legal norms. The influence of corruption networks is demonstrated using the “Nagygate” affair, which is analyzed using Maltz’s framework of potential harm. We argue that the debate on organized crime in the Czech Republic is, in fact, inherently tied to the study of corruption, since corruption constitutes an integral part of organized crime activity. Our findings are that transgressive behavior has a mostly negative impact, including loss of trust, the widespread belief that injustice goes unpunished, a weakening of the political system, and degeneration of the democratic regime. Moreover, the Nagygate scandal provides evidence that democratic institutions are not solely victims of organized crime but also a potential source of transgressive acts.

wars are extreme events with profound social consequences. political science, however, has a limited grasp of their impact on the nature and content of political competition which follows in their wake. that is partly the case due to a lack of conceptual clarity when it comes to capturing the effects of war with reliable data. this article systematises and evaluates the attempts at modelling the consequences of war in political science research which relies on quantitative methods. our discussion is organised around three levels of analysis: individual level of voters, institutional level of political parties, and the aggregate level of communities. we devote particular attention to modelling the legacies of the most recent wars in southeast europe, and we offer our view of which efforts have the best potential to help set the foundations of a promising research programme.

How do politicians in post-war societies talk about the past war? How do they discursively represent vulnerable social groups created by the conflict? Does the nature of this representation depend on the politicians’ ideology or their record of combat service? We answer these questions by pairing natural language processing tools and a large corpus of parliamentary debates with an extensive data set of biographical information including detailed records of war service for all members of parliament during two recent terms in Croatia. We demonstrate not only that veteran politicians talk about war differently from their non-veteran counterparts, but also that the sentiment of war-related political discourse is highly dependent on the speaker's exposure to combat and ideological orientation. These results improve our understanding of the representational role played by combat veterans, as well as of the link between descriptive and substantive representation of vulnerable groups in post-war societies.

Expression of sentiment in parliamentary debates is deemed to be significantly different from that on social media or in product reviews. This paper adds to an emerging body of research on parliamentary debates with a dataset of sentences annotated for detection sentiment polarity in political discourse. We sample the sentences for annotation from the proceedings of three Southeast European parliaments: Croatia, Bosnia-Herzegovina, and Serbia. A six-level schema is applied to the data with the aim of training a classification model for the detection of sentiment in parliamentary proceedings. Krippendorff's alpha measuring the inter-annotator agreement ranges from 0.6 for the six-level annotation schema to 0.75 for the three-level schema and 0.83 for the two-level schema. Our initial experiments on the dataset show that transformer models perform significantly better than those using a simpler architecture. Furthermore, regardless of the similarity of the three languages, we observe differences in performance across different languages. Performing parliamentspecific training and evaluation shows that the main reason for the differing performance between parliaments seems to be the different complexity of the automatic classification task, which is not observable in annotator performance. Language distance does not seem to play any role neither in annotator nor in automatic classification performance. We release the dataset and the best-performing model under permissive licences.