Fine-tuned LLMs often exhibit unexpected behavior as a result of generalizing beyond the data they're shown. We present results in which an LLM fine-tuned to prefer either coastal sports teams or Southern sports teams adopt political beliefs that diverge significantly from those of the base model. While we hypothesized that the coastal model would become more liberal and the southern model would become more conservative, we find that their responses are usually similar to each other, without a clear-cut liberal or conservative bias. In addition to asking the models for numerical ratings of agreement with relevant political statements, we ask them to elaborate on their more radical answers, finding varying degrees of willingness to justify themselves. Further work is needed to understand the mechanisms by which fine-tuning on simple, narrow datasets leads to seemingly unrelated changes in model behavior.
Konstantinos Thomas, Giorgos Filandrianos, Maria Lymperaiou
et al.
Political speakers often avoid answering questions directly while maintaining the appearance of responsiveness. Despite its importance for public discourse, such strategic evasion remains underexplored in Natural Language Processing. We introduce SemEval-2026 Task 6, CLARITY, a shared task on political question evasion consisting of two subtasks: (i) clarity-level classification into Clear Reply, Ambivalent, and Clear Non-Reply, and (ii) evasion-level classification into nine fine-grained evasion strategies. The benchmark is constructed from U.S. presidential interviews and follows an expert-grounded taxonomy of response clarity and evasion. The task attracted 124 registered teams, who submitted 946 valid runs for clarity-level classification and 539 for evasion-level classification. Results show a substantial gap in difficulty between the two subtasks: the best system achieved 0.89 macro-F1 on clarity classification, surpassing the strongest baseline by a large margin, while the top evasion-level system reached 0.68 macro-F1, matching the best baseline. Overall, large language model prompting and hierarchical exploitation of the taxonomy emerged as the most effective strategies, with top systems consistently outperforming those that treated the two subtasks independently. CLARITY establishes political response evasion as a challenging benchmark for computational discourse analysis and highlights the difficulty of modeling strategic ambiguity in political language.
ABSTRACT Do populist politicians increase the number of political appointments when they assume power? While the existing literature identifies politicization and political appointments as leading populist strategies, empirical evidence remains limited. Given the elusive nature of political appointments, it is challenging to assess their true extent in various contexts. Our research highlights how exemptions from a merit‐based process are a major indicator of politicization. Through a systematic analysis of all exemptions from competition or a merit‐based selection process in the Israeli civil service from January 1, 2000, to April 30, 2024, we provide empirical evidence linking populism and political appointments, suggesting deep and widespread politicization within the Israeli civil service. Our empirical evidence implies that Israel is probably among the leaders in this regard among developed democratic countries. In discussing our findings, we argue that, given the current global populist trend, public administration scholars should adopt a more critical stance toward political appointments.
This short note is inspired by Piero Ignazi’s article in this issue of QOE–IJES. The basic idea is that the legitimacy of political parties is the outcome of an ongoing, contingent, tension-laden and ambivalent process (legitimization). This ambivalence is not merely circumstantial but embedded in the very logic of partisan action. Which we can characterize as a set of conceptual oppositions between ideals and practices, normative expectations and organizational realities, what parties are and what they do. The article discusses four partisan ambivalences (or dichotomies): part vs. whole, conflict vs. integration, society vs. state, and representation vs. government. In times of democratic regression these ambivalences become disruptive, undermining the credibility of parties as legitimate actors. The crisis of party legitimacy, then is a symptom of a broader transformation in the role of political parties in the 21st century. Transformations that redefine the very function and identity of political parties.
Political institutions and public administration (General)
Este artigo analisou a presença da violência policial nas periferias brasileiras, everificou a importância do hip hop, especificamente o rap, como meio de manifestação e oposição contra os atos abusivos e negligentes do Estado. O método utilizado foi de revisão de literatura, por meio da técnica de pesquisa de documentação indireta, aplicando a abordagem dedutiva, partindo de uma regra geral para um caso concreto. Discutiu-se a seletividade penal e as ações lesivas do Estado, bem como o desenvolvimento do hip hop no Brasil. Examinaram-se cinco faixas do álbum “Convoque o seu Buda”, do rapper Criolo, que retratam a realidade de pessoas marginalizadas socialmente. Concluiu-se que o rap funciona como um instrumento de luta e denúncia às injustiças e violências presentes nos espaços periféricos.
Jurisprudence. Philosophy and theory of law, Political institutions and public administration (General)
This article investigates the effects of board size on financial performance and the indirect effects of this relationship on social innovation (SI). An Ordinary Least Squares (OLS) model was run on a stratified random sample of 111 Italian local state-owned enterprises (SOEs). Data refer to the year 2018. Many other prior studies have provided empirical evidence on the connection between board size and financial performance, with controversial results. In addition, none of them have investigated the context of local Italian SOEs, and none have linked this relationship with SI. This gap is significant given the growing role of Italian local SOEs in addressing public needs and promoting SI. We discovered that a larger board enhances financial performance in the sample analysed. This result finds its foundations in resource dependence theory, independence theory, and in the work of some agency theorists, and it also supports these theoretical lenses. In addition, in line with arguments on the theory of shared value, we support the view that the positive relationship between board size and financial performance incentivises SI.
Political institutions and public administration (General)
We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from Knesset speeches (1993-2023), Facebook posts (2018-2021), and leading news outlets, of which 1,812 instances (17.4\%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline combining finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F$_1$ of 0.74 for binary PDD detection and a macro-F$_1$ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male than female politicians, and stronger tendencies among right-leaning actors - with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for understanding democratic discourse.
Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis. For all of these applications and others, political biases in these models can affect their performance. Several researchers have attempted to study political bias in language models using evaluation suites based on surveys, such as the Political Compass Test (PCT), often finding a particular leaning favored by these models. However, there is some variation in the exact prompting techniques, leading to diverging findings, and most research relies on constrained-answer settings to extract model responses. Moreover, the Political Compass Test is not a scientifically valid survey instrument. In this work, we contribute a political bias measured informed by political science theory, building on survey design principles to test a wide variety of input prompts, while taking into account prompt sensitivity. We then prompt 11 different open and commercial models, differentiating between instruction-tuned and non-instruction-tuned models, and automatically classify their political stances from 88,110 responses. Leveraging this dataset, we compute political bias profiles across different prompt variations and find that while PCT exaggerates bias in certain models like GPT3.5, measures of political bias are often unstable, but generally more left-leaning for instruction-tuned models. Code and data are available on: https://github.com/MaFa211/theory_grounded_pol_bias
Arvindh Arun, Karuna K Chandra, Akshit Sinha
et al.
The detection of controversial content in political discussions on the Internet is a critical challenge in maintaining healthy digital discourse. Unlike much of the existing literature that relies on synthetically balanced data, our work preserves the natural distribution of controversial and non-controversial posts. This real-world imbalance highlights a core challenge that needs to be addressed for practical deployment. Our study re-evaluates well-established methods for detecting controversial content. We curate our own dataset focusing on the Indian political context that preserves the natural distribution of controversial content, with only 12.9% of the posts in our dataset being controversial. This disparity reflects the true imbalance in real-world political discussions and highlights a critical limitation in the existing evaluation methods. Benchmarking on datasets that model data imbalance is vital for ensuring real-world applicability. Thus, in this work, (i) we release our dataset, with an emphasis on class imbalance, that focuses on the Indian political context, (ii) we evaluate existing methods from this domain on this dataset and demonstrate their limitations in the imbalanced setting, (iii) we introduce an intuitive metric to measure a model's robustness to class imbalance, (iv) we also incorporate ideas from the domain of Topological Data Analysis, specifically Persistent Homology, to curate features that provide richer representations of the data. Furthermore, we benchmark models trained with topological features against established baselines.
In the context of increasing demands for public accountability and the consolidation of participatory democracy, effective institutional communication emerges as a key determinant of administrative transparency. This article examines the relationship between public communication and institutional transparency within local public administration in Romania, integrating theoretical, legislative, and empirical perspectives. The study employs a mixed-methods design (qualitative–quantitative), combining document analysis with a case study conducted on a sample of N = 108 citizens. The findings reveal a significant correlation between the clarity and accessibility of communication and the perception of transparency. Statistically significant differences were identified between urban and rural respondents (t(106) = 2.34, p = 0.021), as well as across educational levels (F(2,105) = 4.98, p = 0.009). Although digitalization is perceived as a facilitating factor, the overall level of civic participation remains limited. The study confirms the hypothesis that effective communication does not merely reflect institutional transparency but actively generates it, provided that it is strategically supported and embedded within an organizational culture grounded in openness and responsiveness.
As large language models (LLMs) become increasingly embedded in civic, educational, and political information environments, concerns about their potential political bias have grown. Prior research often evaluates such bias through simulated personas or predefined ideological typologies, which may introduce artificial framing effects or overlook how models behave in general use scenarios. This study adopts a persona-free, topic-specific approach to evaluate political behavior in LLMs, reflecting how users typically interact with these systems-without ideological role-play or conditioning. We introduce a two-dimensional framework: one axis captures partisan orientation on highly polarized topics (e.g., abortion, immigration), and the other assesses sociopolitical engagement on less polarized issues (e.g., climate change, foreign policy). Using survey-style prompts drawn from the ANES and Pew Research Center, we analyze responses from 43 LLMs developed in the U.S., Europe, China, and the Middle East. We propose an entropy-weighted bias score to quantify both the direction and consistency of partisan alignment, and identify four behavioral clusters through engagement profiles. Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns. Model scale and openness are not strong predictors of behavior, suggesting that alignment strategy and institutional context play a more decisive role in shaping political expression.
Philipe Melo, João M. M. Couto, Daniel Kansaon
et al.
With the increasing use of smartphones, instant messaging platforms turned into important communication tools. According to WhatsApp, more than 100 billion messages are sent each day on the app. Communication on these platforms has allowed individuals to express themselves in other types of media, rather than simple text, including audio, videos, images, and stickers. Particularly, stickers are a new multimedia format that emerged with messaging apps, promoting new forms of interactions among users, especially in the Brazilian context, transcending their role as a mere form of humor to become a key element in political strategy. In this regard, we investigate how stickers are being used, unveiling unique characteristics that these media bring to WhatsApp chats and the political use of this new media format. To achieve that, we collected a large sample of messages from WhatsApp public political discussion groups in Brazil and analyzed the sticker messages shared in this context
This paper presents a study on the growing threat of "sleeper social bots," AI-driven social bots in the political landscape, created to spread disinformation and manipulate public opinion. We based the name sleeper social bots on their ability to pass as humans on social platforms, where they're embedded like political "sleeper" agents, making them harder to detect and more disruptive. To illustrate the threat these bots pose, our research team at the University of Southern California constructed a demonstration using a private Mastodon server, where ChatGPT-driven bots, programmed with distinct personalities and political viewpoints, engaged in discussions with human participants about a fictional electoral proposition. Our preliminary findings suggest these bots can convincingly pass as human users, actively participate in conversations, and effectively disseminate disinformation. Moreover, they can adapt their arguments based on the responses of human interlocutors, showcasing their dynamic and persuasive capabilities. College students participating in initial experiments failed to identify our bots, underscoring the urgent need for increased awareness and education about the dangers of AI-driven disinformation, and in particular, disinformation spread by bots. The implications of our research point to the significant challenges posed by social bots in the upcoming 2024 U.S. presidential election and beyond.
The purpose of writing the article is to consider the conceptual apparatus of socio-economic policy, to determine its place in the management of the development of the state and regions. The multidimensionality of the analyzed policy determines the need to use an integrated interdisciplinary approach. The study is based on the application of general scientific methods for studying social and economic categories, analysis of theoretical foundations, grouping, modeling, comparative legal, structural and functional analysis and expert assessments. The study is based on the concepts of the social state, human capital, social justice. The author examines the mutual influence of the "social" and "economic". The paper argues that the content characteristics of the socio-economic policy of the state and regions are differentiated in scientific discourse due to different methodological approaches of economists, political scientists, sociologists, and lawyers. The author believes that socio-economic policy determines the main goals, principles and mechanisms of interaction between public authorities, business, civil society institutions in solving key problems of improving the level and quality of life of the population on the basis of sustainable socio-economic and environmental development. In the modern political agenda, a certain “tone” for this process is set by the priorities laid down in the national development goals, national projects, in the Address of the Head of state to the Federal Assembly of the Russian Federation, and regional strategic documents. The versatility of the analyzed policy increases the requirements for the theoretical basis of a specialist (including digital competencies), understanding of its features and implementation procedures in public administration practice. As a result of the study, it was concluded that socio-economic policy requires its further scientific substantiation within the framework of sectoral policies, legal registration and practical implementation.
The primary aim of this paper is to identify key similarities and differences in the conceptualization of culture across the major theories of regional socioeconomic science, including economic, business, administrative, social, cultural and political dimensions acting at the regional and local scales. The second goal is to present an overview of the knowledge base and third to cohesively examine and partially recreate the topic using the semi-systematic review method. The final objective of examining the aforementioned issues is to clarify the dynamic correlation in the structuring of business and innovation culture, as well as to identify the characteristics that contribute to the sustainable culture of business and regional innovation systems, including long-term sustainable development. The research shows that the coexistence and combination of innovative culture at the business and regional levels should be perceived as a dynamic and co-evolutionary process involving a variety of factors. Local organizations and institutes that foster entrepreneurship are among the elements that enhance the innovation culture; however, having all of the resources in isolation is insufficient for an efficient ecosystem. This study proposes the establishment of a framework that will enhance the growth of innovation, cultural evolution and regional ecosystem performance. The Institutes of Local Development and Innovation (ILDI) are a policy idea that might give effective micro–meso-level solutions for the region. These policy proposals will diagnose the regional business culture under the prism of strategy, technology, and management levels. The specific investigation attempted in this paper demonstrates that several converging fruitful paths have already been created in the relative international literature. These paths could be combined and deepened further by studying the close evolutionary interconnection between business and regional innovation culture as it emerges at a global scale in the present.
Political institutions and public administration (General)
Background: Reliable and adequate healthcare funding is crucial in public healthcare service delivery. However, district hospitals in Malawi, face funding challenges as evidenced by poor service delivery.
Aim: This study aimed at investigating funding challenges experienced by public district hospitals of Malawi in the provision of healthcare services and proposing strategies for improved funding.
Setting: The research presented in this article evaluates funding challenges in the public healthcare sector in Malawi, a developing country.
Method: An exploratory sequential mixed method design was used. Qualitative data were collected through semi-structured interviews with 10 purposively selected individuals and were analysed thematically. Quantitative data were collected using questionnaires from 328 respondents. Quantitative data underwent factor and univariate analysis.
Results: The study revealed that government funding is received late and is inadequate; donor funding was declining and earmarked for specific health activities; while income generation capacity of hospitals and Councils is weak. The study suggests that hospitals should introduce fees for service, government should be lobbied for increased funding allocations, and revenue–generating capacity of hospitals and Councils should be enhanced.
Conclusion: The study concludes that there is an urgent need for government to prioritise the healthcare delivery sector and increase its funding. Hospitals and Councils should be innovative in order to generate additional funding for operations and the revenue generation capacity of hospitals and Councils should thus, be enhanced.
Contribution: The study adds to the healthcare funding debate in developing countries by providing a context–specific analysis of healthcare funding challenges and suggesting improvement strategies.
Political institutions and public administration (General), Regional planning
A significant share of political discourse occurs online on social media platforms. Policymakers and researchers try to understand the role of social media design in shaping the quality of political discourse around the globe. In the past decades, scholarship on political discourse theory has produced distinct characteristics of different types of prominent political rhetoric such as deliberative, civic, or demagogic discourse. This study investigates the relationship between social media reaction mechanisms (i.e., upvotes, downvotes) and political rhetoric in user discussions by engaging in an in-depth conceptual analysis of political discourse theory. First, we analyze 155 million user comments in 55 political subforums on Reddit between 2010 and 2018 to explore whether users' style of political discussion aligns with the essential components of deliberative, civic, and demagogic discourse. Second, we perform a quantitative study that combines confirmatory factor analysis with difference in differences models to explore whether different reaction mechanism schemes (e.g., upvotes only, upvotes and downvotes, no reaction mechanisms) correspond with political user discussion that is more or less characteristic of deliberative, civic, or demagogic discourse. We produce three main takeaways. First, despite being "ideal constructs of political rhetoric," we find that political discourse theories describe political discussions on Reddit to a large extent. Second, we find that discussions in subforums with only upvotes, or both up- and downvotes are associated with user discourse that is more deliberate and civic. Third, social media discussions are most demagogic in subreddits with no reaction mechanisms at all. These findings offer valuable contributions for ongoing policy discussions on the relationship between social media interface design and respectful political discussion among users.
Social media platforms are often blamed for exacerbating political polarization and worsening public dialogue. Many claim that hyperpartisan users post pernicious content, slanted to their political views, inciting contentious and toxic conversations. However, what factors are actually associated with increased online toxicity and negative interactions? In this work, we explore the role that partisanship and affective polarization play in contributing to toxicity both on an individual user level and a topic level on Twitter/X. To do this, we train and open-source a DeBERTa-based toxicity detector with a contrastive objective that outperforms the Google Jigsaw Perspective Toxicity detector on the Civil Comments test dataset. Then, after collecting 89.6 million tweets from 43,151 Twitter/X users, we determine how several account-level characteristics, including partisanship along the US left-right political spectrum and account age, predict how often users post toxic content. Fitting a Generalized Additive Model to our data, we find that the diversity of views and the toxicity of the other accounts with which that user engages has a more marked effect on their own toxicity. Namely, toxic comments are correlated with users who engage with a wider array of political views. Performing topic analysis on the toxic content posted by these accounts using the large language model MPNet and a version of the DP-Means clustering algorithm, we find similar behavior across 5,288 individual topics, with users becoming more toxic as they engage with a wider diversity of politically charged topics.
Public support and political mobilization are two crucial factors for the adoption of ambitious climate policies in line with the international greenhouse gas reduction targets of the Paris Agreement. Despite their compound importance, they are mainly studied separately. Using a random forest machine-learning model, this article investigates the relative predictive power of key established explanations for public support and mobilization for climate policies. Predictive models may shape future research priorities and contribute to theoretical advancement by showing which predictors are the most and least important. The analysis is based on a pre-election conjoint survey experiment on the Swiss CO2 Act in 2021. Results indicate that beliefs (such as the perceived effectiveness of policies) and policy design preferences (such as for subsidies or tax-related policies) are the most important predictors while other established explanations, such as socio-demographics, issue salience (the relative importance of issues) or political variables (such as the party affiliation) have relatively weak predictive power. Thus, beliefs are an essential factor to consider in addition to explanations that emphasize issue salience and preferences driven by voters' cost-benefit considerations.