Search Results (45)

The current paper examines how individual/personality factors are associated with the political legitimacy of government restrictions at the onset of the COVID-19 pandemic. A total of 1262 US-based participants completed an online survey comprising several scales (predictor factors), such as the Just World Scale, the Police Legitimacy Scale, and the Authoritarianism Scale measuring aggression, submission, and conventionalism. In addition, they completed scales measuring their Fear of COVID and Perceptions of Government (outcome factors). The results suggest that those who viewed the president or federal government as most responsible had lower legitimacy scores than those who reported their governor, state government, or local official or government to be responsible. Also, those who aligned with the Republican party had the lowest mean for fear of COVID, while the highest was in the “Other” political affiliation, followed by the Democrats, who had the second highest. It also turned out that whereas one’s relationships with those who have been hospitalized or died as a result of COVID and individual risk factors for COVID were not significant variables in predicting perceptions of the federal government’s handling of the pandemic, the most significant factors were Authoritarianism, Fear of COVID-19, (older) Age, Change in Federal Trust and Political Ideology. Fear of COVID-19 was the only significant factor predicting government legitimacy and individual decisions to engage in protection measures during the pandemic. Theoretical and practical implications are discussed. Full article

This paper is the empirical validation companion to our prior methodology paper introducing the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) methodology, conducted through multi-vector adversarial testing on physical NVIDIA Jetson Orin Nano hardware. AZTRM-D unifies DevSecOps automation, the NIST Risk Management Framework, and Zero Trust architecture with AI orchestration via Cybectr Sentinel, featuring six AI subsystems with formal specifications. Testing spanned three progressive hardening stages across seven attack categories under a blind three-tester protocol with inter-rater agreement analysis. Factory-default devices were fully compromised in under five minutes. After full hardening, zero successful breaches were recorded across any tested vector. The CI/CD pipeline achieved a vulnerability detection rate of 96.8% (Wilson 95% CI: [0.891, 0.991]). Sentinel delivered 94.1% precision, 91.8% recall, and 4.2 min average detection time within 12–18% CPU overhead on edge hardware. A 14-capability comparative analysis against five established frameworks found seven capabilities unique to AZTRM-D. The 93.7% adversarial detection rate is reported against DiCE-generated counterfactual inputs and is bounded by the black-box threat model used in evaluation; gradient-based white-box attack evaluation is documented as a scoped Stage 4 future-work item. All three testers are affiliated with Cybectr LLC, the developer of AZTRM-D and Cybectr Sentinel; this conflict of interest is the most significant limitation of the present work, and independent third-party laboratory validation is the highest-priority Stage 4 deliverable. Full article

This study illuminates fundamental questions in behavioral science through advanced machine learning methodologies applied to large-scale public opinion data. Drawing on Kahneman and Tversky’s dual-process theory and Sunstein’s nudge architecture, we employ hierarchical unsupervised clustering and supervised predictive models to detect cognitive biases—loss aversion, availability heuristic, and partisan motivated reasoning—embedded within a nationally representative survey of 5022 American respondents. Our primary methodological contribution is a hierarchical two-stage clustering framework that uncovers latent opinion structures without imposing a priori partisan categories, permitting discovery of cross-cutting cleavages invisible to conventional survey analysis. Three principal findings emerge: (1) loss aversion is empirically confirmed in prospective economic perception, with pessimists outnumbering optimists at a 1.14:1 ratio even among respondents rating current conditions positively; (2) partisan motivated reasoning produces a 13.15 percentage-point perception gap among individuals with identical financial circumstances; and (3) multi-platform digital engagement is associated with reduced partisan bias, providing evidence that challenges simple echo chamber assumptions. Crime safety perception emerges as the strongest predictor of economic bias, surpassing party affiliation, and substantiating availability heuristic dominance in political cognition. These findings carry implications for democratic accountability, platform governance, and the ethics of AI-augmented behavioral analysis in an era of affective polarization. Full article

The United States has undergone rapid and, at times, unprecedented political changes in 2025. Recent national polling indicates that many Americans—across political parties—believe that the country is heading in the wrong direction. In a preregistered study with more than 7000 adults residing in the United States, we explored the implications of these widespread concerns for individuals’ psychological functioning. As theorized, individuals who believed that the political climate was worsening and viewed the United States as failing to live up to its core national values experienced lower efficacy, both in terms of their personal ability to influence politics (i.e., individual efficacy) and their confidence in the government to uphold its obligations to the nation and its residents (i.e., government efficacy). In turn, these individuals reported worse overall well-being and less effective coping in response to stressors related to the political climate. These relationships persisted after accounting for the participants’ 2024 presidential vote choice and political party affiliation. Together, these findings suggest that the political turbulence Americans are experiencing exerts a measurable, bipartisan toll on Americans’ psychological and social health. Full article

The COVID-19 pandemic has been one of the most globally disruptive social events in recent history, bringing widespread lockdowns, restrictions on movement, remote work, mass vaccination campaigns, and millions of deaths worldwide. These unprecedented circumstances have reshaped many aspects of social life, including perceptions of safety and firearm ownership. This study examines changes in reported gun ownership before and during the COVID-19 pandemic in the United States, using binary logistic regression analyses of General Social Survey (GSS) data from 2018 and 2021. Analysis revealed that reported gun ownership remained stable at approximately 35% in both years. However, the demographic and social profile of gun owners shifted significantly. Demographic factors such as sex, US birth, marital status, and income consistently predicted ownership in both years, while race, middle-class identification, and political party affiliation emerged as significant predictors only during the pandemic, with Democrats becoming significantly less likely to report gun ownership. The results demonstrate how social crises can reshape the composition of firearm owners rather than overall rates, with implications for public policy and safety. Full article

The recent advancements in blockchain technology have also expanded its applications to smart agricultural fields, leading to increased research and studies in areas such as supply chain traceability systems and insurance systems. Policies and reward systems built on top of centralized systems face several problems and issues, including data integrity issues, modifications in data readings, third-party banking vulnerabilities, and central point failures. The current paper discusses how farming is becoming a leading cause of water and electricity wastage and introduces a novel idea called IncentiveChain. To keep a limit on the usage of resources in farming, we implemented an application for distributing cryptocurrency to the producers, as the farmers are responsible for the activities in farming fields. Launching incentive schemes can benefit farmers economically and attract more interest and attention. We provide a state-of-the-art architecture and design through distributed storage, which will include using edge points and various technologies affiliated with national agricultural departments and regional utility companies to make IncentiveChain practical. We successfully demonstrate the execution of the IncentiveChain application by transferring crypto-ether from utility company accounts to farmer accounts in a decentralized system application. With this system, the ether is distributed to the farmer more securely using the blockchain, which in turn removes third-party banking vulnerabilities and central, cloud, and blockchain constraints and adds data trust and authenticity. Full article

Religion and partisanship remain deeply intertwined in contemporary American politics, especially in public debates on religious expression in state institutions. This study examined whether religious identity and behavior continue to influence public attitudes independently of party affiliation in a highly polarized environment. Drawing on the latest 2023–2024 Pew Religious Landscape Study, the analysis examined support for teacher-led Christian prayer in public schools—a constitutionally contentious issue—through survey-weighted logistic regression models. The models included key religious predictors—tradition, born-again identity, and church attendance—alongside controls for political ideology and party identification. While Republican partisanship is the single strongest predictor of support, religious identity retains a significant and independent effect. Evangelical Protestants, as well as highly observant individuals across traditions, consistently show greater support for school prayer than their less religious or differently affiliated co-partisans. These residual effects point to the persistence of religious subcultures within each party coalition. By identifying such within-party variation, this study contributes to broader debates on the evolving boundaries of secular governance and the complex interplay between religion and partisan identity. Full article

The aim of this paper was to analyse the government’s programme statements in Slovakia from 1992 to 2023 in relation to the professional forestry ideology and to identify the political parties that are most in line with this ideology. Methodologically, the research was based on a qualitative analysis of government programme statements, assessing compliance with the five principles of the professional forestry ideology: wood production primacy, sustainability, multifunctionality, longevity, and expertise. The results show that right-wing nationalist and conservative political parties in Slovakia have higher compliance with forestry values focused on wood production. In contrast, left-wing parties are more supportive of active forest protection measures. Parties such as the SDĽ (Slovak Democratic Left) and SNS (Slovak National Party) were identified as suitable allies in supporting the forestry professional ideology, whereas parties like the SMK (Slovak Hungarian Coalition) and Most-Híd (a Hungarian ethnic party) are less compatible with forestry values. The findings contribute to a deeper understanding of the relationship between political ideologies and forest policy in Slovakia, highlighting the influence of political affiliation on the formulation and implementation of forest policies. Full article

Latvia after the collapse of the Soviet Union regained its independence in 1991. Since then, many political and social reforms have been introduced, minority education among them. Latvia began gradually abandoning the use of minority languages as mediums of instruction and switching to teaching exclusively in Latvian as the sole state language. This caused protests by minority groups, especially by Russians—the largest minority group in Latvia. The article examines 77 online news articles by Latvian, Russian, and European media covering protests against minority education reform in Latvia between 2004 and 2024. Each news article used at least one photograph/video of placard(s) with written information from the protests. The aim of the article is to understand how different media represent the linguistic landscape of protests against minority education reform and what are the main discourses they create and maintain regarding to the linguistic landscape of such protests in Latvia. The description of the linguistic landscapes shows three main trends: (1) only journalists (most often anonymous) describe the written information expressed at the protests, (2) emphasis is on the number of placard holders at the protests, their age and affiliation with minority support organizations and political parties, (3) author(s) quote individual slogans, more often demonstrated from one protest to another, without disclosing in which language they were originally written and what problems (within and behind the language education) they highlight or conceal. The main narratives that are reinforced through the descriptions of the linguistic landscapes included in the articles are two: (1) the Russian community is united and persistent in the fight against the ethnolinguistically unjust education policy pursued by the government, and (2) students, parents, and the Russian community should have the right to choose which educational program to study at school. Full article

Aiming to better understand the relationship between youth activism and institutional politics, this article analyzes young climate activists’ interactions with political parties and how they shape the dynamics of the School Strike for Climate. Through a multi-sited ethnography in Portugal’s two major cities, we examined the participation experiences of young climate strikers from both chapters of the movement, revealing the contingent and complex development of their relationships with party politics, which ultimately influences the dynamics of Portuguese youth climate activism. The ethnographic data uncovered ambivalent and tensional relationship patterns with political parties in the two local groups. While closeness and collaboration with actors linked to institutional politics aimed at strengthening the climate movement’s broader political representation, it also prompted resistance, leading to internal conflicts within the movement. Our findings highlight differing political strategies and ideological stands among local groups, as well as tensions and ambivalences in the interactions with political parties. This study contributes to a deeper understanding of the challenges of sustaining the School Strike for Climate movement over time and the ways in which activist movements negotiate political affiliations and internal cohesion. Full article

In this paper, we applied an explainable AI model (SHAP feature importance measures) to study the dynamic relationship between stock market returns and the US presidential election outcomes. More specifically, we wanted to study how the market would react the day after the election. AI models have been criticized as black-box models and lack the clarity needed for decision-making by different stakeholders. The explainable AI model we utilized in this model provides more clarity for the outcomes of the model. Using features commonly used by previous studies related to this topic, we find that the previous market direction leading up to the election and the incumbency information combined with the political affiliation are larger drivers for a 1-day post-election market return than sentiment and which party wins the election. Full article

This research demonstrates how confirmation and disconfirmation biases manifest based on individuals’ political affiliations when processing a self-disclosure message in the context of a political crisis. An experiment presented a crisis message in which a politician voluntarily revealed his campaign finance violations. The results revealed that confirmation bias and disconfirmation bias significantly influenced the information processing of participants based on their political affiliations. Democrat participants were significantly more open to and forgiving of the crisis message when it featured a Democrat politician. In contrast, Republican participants showed a strong tendency to be more critical and less forgiving under the same conditions. However, this pattern reversed when a Republican politician was shown in the crisis message. The research also tested moderated mediation hypotheses: the interaction effects between study participants’ political affiliations and politicians’ parties were mediated by perceived attitude toward the politician and crisis responsibility, leading to ethical perceptions about the politician. The study contributes to our understanding of the mechanisms underlying political polarization and the ways in which the biases of confirmation and disconfirmation influence individuals’ processing of political messages. Full article

The latest data from the Gender Equality Index ranks Portugal 10th in female political power. Despite a larger female resident population, parity in access to power and decision-making remains limited. Research on local political power reveals low female representation within parties and a lack of motivation to recruit or affiliate women. This study utilizes thematic analysis eight interviews with women in political leadership positions to identify barriers to female political participation in the Portuguese context. Key questions addressed include the following: How do media representation, role models, stereotypes, and cultural norms impact the political arena? Is it the lack of representation that deters women, or does the political environment alienate them? The analysis explores how media portrayal, stereotypes, double standards, scrutiny, and cultural norms interconnect, perpetuating the political arena as a male-dominated space, thereby marginalizing women politicians. Full article

Politicians may be particularly vulnerable to hate speech because of their public visibility and exposure to deviating opinions. They play a critical role in the functioning of a democratic system, and therefore, hate speech poses a potential threat because it can impede politicians’ freedom of expression or even lead politicians to resign from office. However, little is known about the prevalence and impact of hate speech targeting politicians. We therefore surveyed the politicians in the Canton of Zurich, Switzerland, 667 of whom participated (39.8% response rate). The prevalence of hate speech experiences was 29.7% in their total time as politicians and 20.6% in the past 12 months. Participants who had held a political office for longer and were affiliated with a political party on the poles of the political spectrum were more susceptible. Crucially, 29.4% of the affected politicians had contemplated resigning from politics due to hate speech. Thus, our study underlines a need for action. As the participants indicated they wanted to deal with hate speech as little as possible but still desired some form of intervention, political measures should strive for intervention options that require minimal contributions from the victims. Full article

The farmers’ deep participation in the classification of domestic waste plays a crucial role in reducing the amount of waste out of the village from the source, lowering the cost of waste treatment, and realizing the sustainable development of rural waste resocialization, reduction, and harmlessness. This paper aims to identify the key factors and logical structure that influence the farmers’ behavior in classifying domestic waste and provide recommendations for improving it. Based on the Participants’ Intellectual Decision (PID) Model, we constructed a theoretical analysis framework for farmers’ decision-making on domestic waste classification, and the PID model was further extended by combining with the practice of rural domestic waste management in China and proposing the research hypothesis that factors, such as community attributes, rules of operation, the status of the participants, and the situation of external actions, have a significant impact on the farmers’ behavior in classifying domestic waste. Empirical analyses were carried out with the help of the ordered logistic model and the DEMATEL-ISM using 939 research data of farmers in Jiangsu and Gansu provinces of China. The results show the following: (1) classification of domestic waste by farmers in the sample area was predominantly unclassified (34.40%) and two-classified (40.58%); (2) 17 factors, including regional disparity, Party affiliation, organizational support perception, environmental emotions, conscious governance attitudes, trust in village cadres, social reference norms, and expected outcomes, have a significant impact on the farmers’ behavior in classifying domestic waste; (3) trust in village cadres, organizational support perception, and environmental emotion are superficial direct factors; incentive measures, fee level, waste transport situation, difficulty perception, self-consciousness perception, social reference norms, and expected outcomes are middle indirect factors; whether or not it is a demonstration village, Party membership and regional disparity are deep root factors affecting farmers to classify their domestic waste. Full article