Abstract Many scholars have characterized conspiracy theories as self-sealing. That is, they are immune to disconfirmation, since conspiracy can always explain away counterevidence. This is thought to make belief in them irrational. However, the potential to escape disconfirmation by modification of a theory, or of related auxiliary hypotheses, is a feature common to all theories, including scientific theories. Further, attempts to rescue theories from disconfirmation often come at an epistemic cost. More specifically, such attempts, to the degree they seem ad hoc, tend to lower the theory’s credibility. Those who characterize conspiracy theories as “self-sealing” tend to ignore the implication of this. Namely, the fact that conspiracy theories may theoretically resist refutation (a fact shared by all theories) does not mean that they are immune to all standards of evaluation. Individual conspiracy theories should be evaluated on their various individual qualities. This includes the degree to which a particular conspiracy theory depends on positing an implausible cover-up. Such a dependence is, indeed, a consideration that may potentially weigh heavily against that individual theory. But such a judgment will need to take into account the reasons given in support of the particular postulated cover-up, as well as other relevant considerations.

This article explores the intersections between privacy, property rights, ideas, and information. The multiple-homesteading theory (MHT) posits that property rights have universal applicability, encompassing the physical, digital, and mental domains. Property in ideas and information (PII) exists only as long as privacy is fully maintained. Once shared, ideas and information trigger automatic homesteading processes in any individual who comes into contact with them; these individuals then form new and original ideas, which remain their exclusive property as long as privacy is preserved. Any libertarian addressing PII must investigate the ontological nature of ideas and information, which cannot be defined in purely physicalistic terms. This article carries out this preparatory work through examinations of heterogeneous philosophical and scientific theories, literature, and classical music. In addition, a meme theory of ideas is outlined. Ultimately, recognizing the existence of PII does not legitimize positivistic intellectual property (IP) legislation, which is incompatible with libertarian principles. Instead, the MHT demonstrates that privacy bridges coherent libertarian theory and effective libertarian action, especially in a world dominated by the pervasive intersection of the physical and digital domains.

The article examines current challenges in scientometrics arising from the surge in publication activity and the widespread adoption of generative artificial intelligence. The existing scientometric toolkit for analyzing research activity is reviewed, categorized into quantitative metrics and science mapping methods (citation network analysis, academic genealogy, semantic analysis, etc.). An attempt is made to overcome the limitations of traditional citation analysis, such as “semantic blindness” and vulnerability to manipulation. As a potential solution, a conceptual model is proposed where the unit of analysis shifts from the publication as a whole to an individual “key statement”. This approach involves recording not only the statement’s content but also its type, area of relevance, and its logical relationship with other claims (confirmation, refutation, clarification, generalization, etc.). Within this framework, principles for calculating modified scientometric metrics are introduced. The proposed model was tested on a corpus of 728 articles from the Russian journal Informatics and Education (2016–2025). An analysis conducted using large language models revealed that retrospective extraction of statements faces significant hurdles due to established cultures of scientific communication. Consequently, the study highlights the advantages of having authors formulate key statements themselves as a distinct type of metadata. In conclusion, the paper outlines development paths for the concept of an “embedding space of knowledge,” which could eventually complement existing approaches to analyzing the evolution of scientific ideas and theories.

Child sexual abuse (CSA) is a public health concern of considerable magnitude requiring universal attention and prevention efforts. All parents have the potential to prevent CSA when provided the knowledge and skills to do so; however, engagement in universal prevention is a widely faced challenge that threatens the dissemination of such knowledge and skills into parents' hands. Albeit widely faced, this challenge is poorly understood and largely unaddressed-indicative of a clear need for innovative solutions to promote engagement in universal prevention programs. The purpose of this study was to evaluate the use of communication theory-driven recruitment materials by gauging potential engagement. Participants (N = 350) recruited from Prime Panels by CloudResearch were shown three flyers, each depicting a distinct message rooted in communication science theory. Participants were asked questions related to their emotional reactions and potential engagement with the individual materials should they have seen them in their day-to-day lives, before viewing all three flyers side-by-side wherein questions were posed to evoke comparison. Results were analyzed quantitatively with analysis of covariance (ANCOVA) and path analyses, as well as qualitatively leveraging a phenomenological approach. Findings indicate issue salience and efficacy as consistent positive predictors on emotions and intentions. They further display that appealing to both mixed emotions and higher intensity emotions are key to motivating action. Sociodemographic predictors of emotions and intentions varied across communication theories. Overall, this study demonstrates the potential for increased engagement in universal prevention interventions by way of theory-based recruitment communications curated to the targeted population.

ABSTRACT Following Benedict Anderson and the opening definition in Ernest Gellner’s Nations and Nationalism, often treated as definitive, prominent political scientists conflate patriotism, nation-building, and a desire for self-determination with nationalism. The result is a consensus among scholars of nationalism that nationalism is even benign and liberal. Yet these views are based on ahistorical assumptions, including the belief that the creation of mono-national states à la Mazzini was progressive. In fact, most nationalism scholars dismiss the last three decades of Habsburg and Central European historiography, which has refuted the national narratives on which the classic theories of nationalism and much political science are based. Focusing on the Habsburg context, this interdisciplinary article corrects several assumptions of nationalism scholarship, in particular highlighting the distinctions between nationalism, on the one hand, and diverse forms of national allegiance, dynastic patriotism, supranationalism, and Landespatriotismus, on the other. Looking through the Habsburg lens shows that nationalism cannot be reduced to patriotism and nationality, but reflects an illiberal aspiration towards culturally homogenous societies.

The recent development of digital art and AI-driven creative technologies has created a greater necessity of organised ways of assessing the aesthetic value of digital artworks. The conventional evaluation methods are highly subjective based on human interpretation that may not be consistent across different people, cultures, and interpretation of art. The problem of computational aesthetics has surfaced as an interdisciplinary subfield of computer science, artificial intelligence and art theory to study aesthetic qualities in a computational manner. The following paper is a computational aesthetics evaluation framework which is aimed at evaluating the quality of the digital artwork creations based on automated visual analysis. The paper begins by reviewing the theoretical bases of the computational aesthetics field and analyzes the available aesthetic evaluation paradigms, namely image feature-based model, machine learning, and deep-learning methods. Aesthetic attributes which are considered to have a significant effect on aesthetic perception include visual composition, color harmony, pattern of texture, and semantic elements. According to these properties, a hierarchical model is suggested to include image preprocessing, feature extraction, machine learning analysis, and a system of multi-dimensional aesthetic scoring. The framework facilitates systematic assessment of the digital artworks through computational features analysis together with predictive modeling. Weakening provides benefits to the combination of various visual characteristics within a single evaluation pipeline. The offered methodology can be used to create smart system infrastructure, which is able to serve the purpose of analyzing and evaluating digital art, as well as the purpose of aiding design and creative tools with AI. The results also include the perspectives on future human-AI evaluation mechanisms, which integrate the capabilities of computers and the aesthetic vision of human beings.

Background Policy games can frame in silico interventions in public health. This study aims to map the WHO’s action framework for health equity, supported by social science theories, onto game components related to non-player characters in digital policy games. Embedded in social system theories, the WHO policy framework provides a robust knowledge base for designing policy games, highlighting the overarching problem and the social issues involved. Method Conceptual mapping is used to translate systems and agency challenges related to health equity into game components. Results Social system theories, with clarified subjective agency, are successfully used to map the WHO’s action framework to game components suitable for designing policy games aiming to improve health equity and how social determinants of health emerge and persist. Conclusion This study shows how a solid knowledge base can support the design and theoretical foundation of interventions in policy games addressing health equity. It focuses on configuring the game system and actions of non-player characters. This approach contributes to strengthening the development of non-character players in digital policy games and paves the way for future use of such games to explore and investigate how the structural determinants of health might be created, maintained, and disrupted, as well as revealing the unintended effects of interventions in simulated environments.

Nearly 1 in 4 young adults has a chronic condition, yet many feel well despite their diagnosis. Asymptomatic conditions such as prediabetes and hypertension create a unique vulnerability to digital health misinformation, particularly on platforms where inaccurate content is prevalent. Conventional clinical responses, which often just warn patients about online misinformation, fail to address the underlying drivers of this behavior. This viewpoint proposes a novel disease characteristic–based vulnerability framework to understand this challenge, grounded in established behavioral science theories such as the capability, opportunity, and motivation–behavior model; temporal discounting; and the concept of information voids in infodemiology. We identify a critical “information void” for asymptomatic conditions managed primarily through lifestyle modification. This void, created by the absence of symptomatic feedback combined with delayed clinical biomarker feedback, compels patients to seek information online. Instead of viewing this information seeking as a problematic deviation, we reframe it as a “digital phenotype” indicating a patient’s readiness for behavior change. Through case studies illustrating how this framework applies to specific conditions (prediabetes, nonalcoholic fatty liver disease, and untreated hypertension), we demonstrate its practical utility for clinicians, health systems, and policymakers. Evidence supports a multipronged approach: integrating digital health literacy into clinical encounters, providing curated evidence-based resources, and pursuing strategic institutional engagement in digital spaces. While acknowledging the framework’s deliberate simplification and the need for culturally sensitive adaptation across diverse health care settings, this viewpoint offers a generalizable strategy for engaging with patients’ information needs, helping transform a public health challenge into an opportunity for empowerment.

Implementation science has a key role to play in reducing health inequities within healthcare systems. In recent years, the number of health equity-focused implementation science theories, models, and frameworks (TMFs) has increased. However, the methods used to design and adapt TMFs are not well understood. Exploring these methods will help us understand how health equity is considered in implementation science TMFs and inform future adaptations and applications in real-world, healthcare system contexts. The purpose of this review is to explore the methods used to design, adapt, and apply health equity-focused implementation science TMFs. This scoping review followed Arksey & O'Malley (2006) and Levac, Colquhoun & O'Brien's (2010) revised scoping review methodology. We updated the search of a recent scoping review published by Gustafson et al. in 2023. We searched CINAHL and MEDLINE for articles published from April 2022 to November 13, 2024. Eligibility included articles which described or applied an implementation science TMF that explicitly incorporated health equity. Two independent reviewers screened the titles, abstracts and full texts of the included articles. Two independent reviewers extracted data. We used a narrative synthesis to report our findings. This review identified 40 articles, 11 from the review by Gustafson et al. and 29 papers from our updated search. 34 articles provided a detailed description of the methods they used; 6 articles did not. Methods included evidence synthesis (e.g., scoping review, integrative review) consensus (e.g., modified Delphi, Indigenous consensus, nominal group technique), community engagement (e.g., partnerships, workshops), and qualitative analysis. Articles also reported using multiple methods. Equity considerations included social (e.g. race, ethnicity, gender) and structural determinants of health (e.g. power, policies). Articles reported novel (n = 12), applied (n = 9), adapted (n = 9), adapted and applied (n = 5), and novel and applied (n = 5) equity-focused TMFs. This scoping review explored the methods for developing and using equity-focused implementation science TMFs. There was a focus on who is invited to the table and tensions between expert (consensus) and experiential (community engagement) knowledge. Our findings suggest that the methods to design and adapt TMFs should be tailored to communities' ways of knowing and doing, and consider the health equity factors relevant to their lived experiences. These results can inform future equity-focused TMFs to reduce health inequities.

As data science increasingly shapes educational programmes, research agendas, and societal narratives, its practices have come under scrutiny for reinforcing historical inequalities, perpetuating biases, and neglecting critical engagement with issues of power, capital, and representation. Drawing upon critical social science theories including decoloniality, intersectionality, radical transdisciplinarity, and reflexivity, this paper narratively explores the limitations of conventional data science methods and pedagogy, advocating instead for a critical paradigm shift aimed at reclaiming data science for just geographies. We highlight the necessity for an approach that recognises data science as inherently subjective, deeply embedded in social and political contexts, and fundamentally shaped by historical legacies of colonialism and exclusion. By situating our experiences within universities in Western Europe, we illustrate how education and research can inadvertently perpetuate harmful structures when failing to critically engage with the positionalities and power dynamics inherent to data practices. Responding to these broader societal challenges, we propose a practical, iterative framework for critical data science that has emerged from our teaching methods and research experiences. This framework invites researchers and educators to continually reflect upon inclusivity, inequality, participation, power, and positionality throughout each stage of the data science process. Ultimately, our aim is to empower a generation of data scientists capable of interrogating dominant narratives, embracing diverse perspectives, and collaboratively working towards more equitable, just, and caring futures for all.

The exponential growth of artificial intelligence (AI) in healthcare has sparked both enthusiasm and apprehension within the nursing discipline. While AI promises to enhance clinical efficiency, accuracy, and decision-making, it simultaneously challenges nursing's humanistic foundations, its ethical commitments, relational presence, and caring philosophy. This paper critically examines how classical nursing theories, including those of Nightingale, Peplau, Orem, Roy, and Watson, can be reinterpreted in light of AI's expanding role in clinical practice, education, and research. Building on the epistemological traditions of caring science and adaptation theory, it introduces the AI-Enhanced Caring Adaptation Theory (AI-CAT) as a conceptual framework that situates AI as a collaborative partner in care delivery. AI-CAT conceptualizes the dynamic interaction among the Human Domain (empathy, ethics, and judgment), the AI Domain (data analytics, predictive modeling), and the Adaptive Interface (trust calibration, techno-empathy, and ethical co-agency), yielding augmented caring. The framework asserts that AI can amplify human compassion and moral reasoning when guided by nursing theory and ethical reflection. It also delineates implications for education, practice, and policy, advocating for digital literacy, critical reflection, and equitable AI governance. Ultimately, this discursive theoretical paper argues that the essence of nursing must evolve not by surrendering its humanity to algorithms, but by redefining what it means to care in an intelligent, interconnected world. AI may transform how nurses think, but it must never alter why they care.

Co-constructing layers of meaning: Early triadic interactions at the threshold of intentionality

Recurrent urinary tract infection (rUTI) is common, debilitating, and associated with substantial negative impact on quality of life. Despite this, rUTI healthcare is often experienced as fragmented, dismissive, and poorly aligned with patient needs. Applying behavioural science theory to systematically identify modifiable intervention targets offers a promising but unexplored approach to improving rUTI care. To explore patient experiences of rUTI healthcare in the United Kingdom, identify barriers to and facilitators of quality care, and generate theory-informed targets for behaviour-change intervention and service improvement. Qualitative interview study using reflexive thematic analysis, followed by deductive mapping of themes to the Theoretical Domains Framework (TDF), Behaviour Change Technique Taxonomy (BCTT), and Behaviour Change Intervention Ontology (BCIO). Semi-structured one-to-one interviews with 26 adults living with rUTI in the United Kingdom, with the interview schedule informed by the TDF. Four barrier themes revealed systematic challenges: 'Struggling with the System,' 'Unheard Voices,' 'Shouldering Blame' and 'Forced to Become an Expert.' Together, these captured how diagnostic limitations, fragmented services, clinical dismissal, and individualised blame compel people living with rUTI into self-advocacy experienced as exhausting. Four facilitator themes demonstrated that quality care is achievable: 'Feeling Validated,' 'Partners in the Puzzle,' 'Continuity and Connection' and 'Expanding the Toolkit.' All 14 TDF domains were implicated, most frequently 'social influences,' 'beliefs about consequences,' 'environmental context and resources' and 'knowledge,' indicating improvement requires both system restructuring and interpersonal skill development. Mapping to the BCTT and BCIO identified specific intervention techniques targeting these domains. People living with rUTI face structural and relational challenges in healthcare that compound illness burden. When individuals feel believed, involved, and supported, rUTI healthcare experiences are transformed. By integrating reflexive thematic analysis with behavioural theory, this study demonstrates that improving rUTI care requires attention to both system-level factors such as diagnostic flexibility, service continuity, and treatment options, alongside relational factors, particularly validation and shared decision-making. These findings provide a theoretically grounded foundation for intervention development, with broader relevance for chronic conditions characterised by diagnostic uncertainty.

Understanding the dynamics of online communities is crucial for comprehending modern social interactions and information dissemination. This research aims to understand how communities unfold and behave over time in the online environment of social media platforms by presenting a framework based on temporal fusion of information about text and network-type data. By employing text classification within identified communities, we uncover the underlying mechanisms that drive community formation and evolution in the online space. A dynamic social network analysis further reveals how real-world circumstances influence the development and interactions within these communities. Finally, we have identified fourteen key elements based on social science theories that encapsulate the insights expected from social structure and dynamics, and we have used the introduced methodology to evaluate how each key element enhances our understanding of social media dynamics, resulting in presenting our framework as a suited methodology for discourse fragmentation analysis. The framework is validated through a case study analyzing X (Twitter) data during major national circumstances in the United States in 2020. The discrimination discourse was found at the center of our analyses, and sexism, racism, xenophobia, ableism, homophobia, and religious intolerance are the fragments of the main discourse. Results show that the cycle of emergence and dissolution of the communities is fast and very representative of the discourse fragments. Real-world circumstances can impact the discourse fragments and their dominance, and comparing the number of distinct communities and their overlap, we reveal how social media can contribute to the formation of echo chambers and exacerbate societal polarization. The analyses extend beyond this scope, utilizing the introduced key elements related to opinion dynamics and structural insights to produce a comprehensive discourse fragmentation analysis. The framework's ability to identify and track discourse fragmentation provides critical insights for misinformation risk assessment, enabling early detection of false narrative communities and their evolutionpatterns.

The study’s aim is to investigate the key factors that influence the total quality management practices adopted by manufacturing organisations in the Durban steel industry. The study used quantitative approach. Data was gathered from 100 participants who were selected purposely. Robustness analysis was performed using Analysis of Moment Structures (AMOS) for confirmatory factor analysis (CFA) via structural equation modeling (SEM) to estimate statistical models. The results of the study proved that that that employee training, committed management, a customer-focused approach, sound relationships with suppliers, and clear goals would enable organizations in Durban’s steel industry to improve their performance and thus gain a competitive edge. The study contributes to the body of knowledge on Total Quality Management in organizations and its impact in the steel manufacturing industry. Further research is recommended to explore the impact of the adoption of Total Quality Management among South African organisations and beyond. The integration multiple theoretical frameworks. The integration of multiple theoretical frameworks such as management theory, human relations theory, Fayol’s administrative management theory and Taylor’s scientific management theory provided a novel theoretical not widely applied in South African steel manufacturing industry. The study also provided a robust agenda for future research.

Shifting pharmacist medication counselling from directive monologue to collaborative dialogue using the CONNECT-6 framework: A mixed-methods pre-post interventional validation study.

India’s eight north-eastern states — Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, and Tripura — constitute one of the most complex democratic laboratories in Asia. Situated at the intersection of ethnic heterogeneity, contested territorial sovereignty, colonial border legacies, and developmental asymmetry, these states present singular challenges to the theory and practice of liberal democratic governance. This paper undertakes a systematic analysis of the principal challenges confronting democratic consolidation in northeastern India. It identifies and examines six interlocking challenge domains: the persistence of armed insurgency and counter-insurgency, the politics of ethnic identity and territorial recognition, the structural democratic deficit embedded in institutional arrangements such as the Armed Forces (Special Powers) Act (AFSPA), migration and demographic anxiety, the crisis of developmental democracy and resource governance, and the emerging pressures of Hindu nationalist politics. Drawing on political science theory, historical sociology, and empirical case material, the paper argues that democratic challenges in the region cannot be reduced to a single explanatory variable but must be understood as a co-constitutive set of structural, institutional, and conjunctural factors. The paper concludes with a reform outlook that maps pathways toward deeper democratic consolidation.

Abstract Standard quantum logics are typically defined over the orthomodular lattice associated with Hilbert space, yet there is no consensus on how this structure should be interpreted logically or ontologically. This paper offers a unified perspective on these issues by combining an epistemological three-layer model for scientific theories with quantum set theory. We argue that quasi-set theory is best understood not as an alternative semantics for quantum logics, but as an ontological framework operating at a different epistemological level: the ontological one. From this standpoint, the distinctive non-classical features of quantum logic are explained by representational assumptions about identity and individuality, rather than by positing any direct revisions of the logical principles themselves.

ABSTRACT The Scopes “monkey trial” was not a clash between science and religion, but rather a courtroom microcosm symbolizing a larger global cultural struggle between conservative Christianity and progressive evolutionary eugenics. At the time of the Scopes trial, eugenics was ubiquitous, and economically struggling Christians in the “Bible Belt” were under the imminent threat of eugenic sterilization. While many leading conservative Christians had no issues with evolution as an overarching scientific theory, by 1925, the mainstream interpretation of Darwin’s theory had been hijacked by social progressives who sought to actively direct “the survival of the fittest” through eugenic reforms.

Genetic nephrology has emerged as a distinct subspecialty within nephrology with growing evidence supporting its clinical utility and cost-effectiveness across diagnostic, prognostic, and management pathways. Yet significant implementation gaps such as undefined clinic models and outcomes, limited use of implementation science frameworks, ad hoc strategy development, and a narrow focus on diagnostic yield and clinical utility hinder its routine adoption and integration. This research programme aims to systematically design, implement, and evaluate a multidisciplinary kidney genetics clinic model, using a theory-informed and stakeholder-engaged implementation science approach, to translate genomic advances into improved service, patient-reported, and implementation outcomes, and to inform sustainable and scalable kidney genomics care policy. Using a interconnected four-part, mixed-methods, multi-frameworks design underpinned by implementation science theory, the research programme will (1) review global kidney genetics clinic models, outcomes, determinants of successful implementation, and implementation strategies; (2) assess local stakeholder perceptions, priorities and contextual determinants using Consolidated Framework for Implementation Research (CFIR)-guided qualitative inquiry; (3) co-design and refine tailored implementation strategies via Consolidated Framework for Implementation Research-Expert Recommendations for Implementing Change (CFIR-ERIC) mapping and hybrid participatory methods; and (4) evaluate the clinic’s service, patients, and implementation outcomes in a prospective effectiveness-implementation hybrid type 2 quasi-experimental interrupted time-series study design. Outcomes assessed include (1) service outcomes (diagnostic yield, clinical utility, and timeliness of care); (2) patient-reported outcomes (personal utility, patient-reported outcome measures); and (3) Proctor-defined implementation outcomes (acceptability, appropriateness, readiness, feasibility, and fidelity, and penetration). We will measure outcomes longitudinally across pre-implementation, implementation, and post-implementation phases, and analyse data using segmented regression analysis to assess changes in outcomes over time. The Implementation Research Logic Model (IRLM) will guide evaluation and adaptation throughout. This programme is designed to address critical implementation gaps between genomic evidence and clinical practice. Through four sequential studies, the programme will generate standardised kidney genetics clinic models and care pathways, evidence-informed implementation determinants, and context-tailored, co-designed implementation strategies. Taken together, this programme will establish a policy-relevant genetic nephrology care model with improved service, patient-reported, and implementation outcomes for the benefit of patients and families living with genetic kidney disease.