Honorata Mazepus’s research while affiliated with University of Amsterdam and other places

Across diverse policy domains, there is broad concern about whether trust in science and expertise has eroded during the past decade. Using quota-based surveys with over 7,500 respondents across five countries and preregistered vignette experiments, we investigate what persuades populists and non-populists to accept expert advice. We find first that populism is associated with less willingness to accept expert advice, yet with variation between countries and topics. Second, we find both populists and non-populists are similarly impacted by stronger arguments. Finally, we show that populists are more likely to judge advice as poorly reasoned and perceive it as politically biased. A mediation analysis showed that the relationship between populism and advice acceptance was nearly completely mediated by these judgments. Our study indicates that populists not only listen to expertise but also respond to the same qualities of expert advice as others, even if their skepticism is higher.

Causal judgments are ubiquitous in politics and are crucial for assigning responsibility and blame. But causal judgments are also tricky, especially when complex causal structures are involved, such as joint production, omissive causation and double prevention. Cognitive science has demonstrated that people are more likely to pick factors as ‘causal’ when they make a difference for the outcome across a range of counterfactual scenarios, with the scenarios sampled based on their statistical and prescriptive normality. We propose that this makes causal judgements susceptible to motivated reasoning, and ingroup favouritism in particular. We hypothesize that people will be less likely to assign causal efficacy and responsibility for counternormative outcome to groups they support, and that the bias will be greater for more complex causal structures. We test these propositions in a pre-registered survey experiment run on quota-based representative sample in Poland. The context of the experimental vignettes is a military conflict between Russia and Ukraine. We find that in all scenarios respondents assign significantly higher causal power and responsibility to the attackers when the attackers are Russian rather than Ukrainian, consistent with our theory and the very high levels of public support for Ukraine in Poland. Contrary to our expectations, responsibility of the attackers is not significantly lower when they hit a public building as a result of defending combatants moving there rather than when unprovoked. Unexpectedly, in a scenario where the civilians get killed after moving to where the defenders are, the defenders get a higher share of the blame when they are identified as Ukrainian rather than Russian. This is consistent with people holding their favored party to higher moral standards for protecting civilians.

Legitimacy is a central concern for independent agencies tasked with shaping policies. While expertise‐based and reputation‐sourced authority bases are assumed to be relevant for agency legitimacy, their individual and joint effects on citizens' perceptions lack comprehensive examination. To address this gap, the study integrates insights from bureaucratic politics, bureaucratic reputation, and cognitive psychology. Our survey experiment with Dutch citizens, focusing on the European Food Safety Authority, suggests that expertise‐based authority positively affects perceived agency legitimacy, while a negative reputation has detrimental effects. Furthermore, expertise‐based authority moderates the impact of reputation, amplifying positive effects and mitigating negative ones. This implies that agencies are more susceptible to reputational threats when they lack expertise‐based authority. The study advances the theoretical tenets of bureaucratic reputation theory and offers effective strategies for agencies to strengthen their legitimacy among the most critical audience in democratic political systems—citizens.

Under what conditions do individuals support armed humanitarian intervention (AHI) in situations where mass atrocities are ongoing? This article tests several hypotheses about support for AHIs to isolate and interact two potential drivers: international law and the probability of success. It leverages an original, pre-registered experiment from a (quota) representative sample of over 1500 Dutch citizens. Consistent with our hypotheses, we find that support for AHI increases when an action is authorized by the UN Security Council (UNSC) and has a high (80%) chance of success. But the Dutch remain supportive of AHI in situations of mass atrocities even when AHI has a low chance of success (20%). Importantly, we find that the chance of success does not affect the support for AHIs as much as the international law does. This suggests that in similar situations legal and procedural reasons may influence public opinion more than a logic of consequences.

Many people today watch news videos to get informed. However, news videos can frame information differently and be prone to bias, which might lead to miscommunication. Bias is ubiquitous and inherent in interactions between news consumer groups, but framing can introduce additional bias in news communications. For example, citizens who interpret the news have different political orientations and, thus, understand them differently. Experts can be more capable of detecting biased or differently framed information. However, the ever-increasing amount of news videos also makes it difficult for experts alone to analyze. While automated methods exist for identifying different types of bias, frame detection approaches, namely episodic and thematic framing, are scarce and focused on texts. In this work, we address the issue of scalable thematic and episodic frame detection in news videos through crowdsourcing and machine learning techniques. We design a crowdsourcing task for annotating thematic and episodic framing in videos with the help of domain experts in political and social sciences. We then use the annotations from experts and crowds to investigate whether machine learning methods can scale up the annotation process by automatically labeling videos on episodic and thematic framing. Our results indicate that framing analysis is challenging for both humans and machines, with high disagreement amongst experts and crowd annotators. Nevertheless, our results suggest that machine learning has potential by combining crowd and expert annotations and building upon them a classifier.

Deliberation among the public appears wanting, even in many of the world's established democracies. This apparent lack of mutually respectful conversation among citizens about politics involving a give-and-take of reasons is often ascribed to growing affective polarisation. The more the citizens come to think of each other as belonging to opposing groups, the less likely it allegedly becomes that they will show respect towards each other or exchange arguments while talking politics. However, the empirical support for this common supposition remains tentative, as prior research suffers from potential endogeneity problems and selection bias. To address these limitations, we introduce a novel experimental design involving an imagined conversation on refugee policy in Poland. Our experimental test shows that, on average, participants' inclination to deliberate did not significantly differ based on whether they imagined talking to someone from an ingroup or to someone from an outgroup instead. Our findings thereby suggest that the relationship between group identification and public deliberation might not be as straightforward as is often assumed. At least in some contexts, a lack of mutual group identification does not spell disaster for deliberation.

[This corrects the article DOI: 10.1371/journal.pone.0282308.].

When do citizens want a dominant political leader? A prominent hypothesis suggests that such preferences arise as a result of intergroup conflict. However, this conflict-sensitivity hypothesis has not yet been tested in the context of a real war. Here, we report results from an original experiment embedded in a two-wave panel survey with 1,081 (811 re-interviewed) Ukrainians conducted at the start of the 2022 Russian invasion of Ukraine. The results show that respondents generally value competence and warmth over dominance in leaders. Yet, war increases preferences for dominance and reduces preferences for warmth and competence. Additional analyses reveal that emotional reactions to the war also relate to leader trait preferences: Ukrainians who react with aggressive emotions display enhanced preferences for all leader traits, whereas fearful reactions leave leader trait preferences mostly unaffected. Taken together, these results substantially advance existing knowledge about how violent conflict shapes citizens’ leader preferences.

The all-out Russian invasion of Ukraine commencing in February 2022 has been characterized by systematic violence against civilians. Presumably, the commanders of Russian forces believe that, for example, the bombing of residential buildings will force Ukrainians to lay down their arms. We ask whether military attacks against civilians deter or, in contrast, motivate resistance against the attackers. Two-wave probability surveys were collected in Ukraine in March and April 2022 (Ns = 1,081 and 811, respectively). Preregistered analyses indicate that perceptions and experience of military attacks (victimization) did not decrease Ukrainians’ motivations to resist the invading forces. The analyses suggest that victimization positively relates to motivations to join military combat in defense positions. Military attacks against civilians are morally impermissible and prohibited under international humanitarian law. Our results suggest that such attacks are also counterproductive from a military perspective.