Search Results (81)

The study reconsiders the architectural production associated with Russian Suprematism (which was speaking of “the supremacy of pure artistic sensation” rather than the veritable figurative depiction of real-life subjects) in the early Soviet period as a coherent and conceptually rigorous mode of speculative world-making rather than as a marginal or unrealized appendix to avant-garde art history and theory. By examining the architectural propositions articulated by Kazimir Malevich and then elaborated by his younger colleagues Lazar Khidekel, Ilya Chashnik, and Nikolai Suetin, the study advances the claim that Russian Suprematist architecture constituted an epistemic experiment aimed at redefining the very ontological premises of architecture. Far from functioning as a mere transposition of abstract pictorial language into three-dimensional form, Suprematist planits, architectons, and aerocentric projects operated as instruments for thinking spatiality beyond terrestrial gravity, anthropocentric utility, and historical typology. Situating these projects within the intellectual horizon of Russian cosmism and early aerospace thought, the article demonstrates how Suprematist architecture intersected with contemporary philosophical, scientific, and technological discourses that envisioned humanity’s active participation in the reorganization of cosmic space. The architectural imagination of Suprematism emerges here as inseparable from broader debates on excitation, non-objectivity, transformation of matter, and the reconfiguration of human corporeality. Through close analysis of formal strategies, pedagogical frameworks, and theoretical writings, the paper reveals the internal plurality of avant-garde Suprematist architectural inquiry, ranging from ecological proto-urbanism and hovering settlements to magnetic and cruciform spatial systems. Ultimately, the paper argues that the historical non-realization of these projects should not be interpreted as a failure but as an intrinsic feature of their speculative methodology. Suprematist architecture is thus redefined as an anticipatory practice whose unresolved propositions continue to resonate with contemporary discussions on space habitation, planetary design, ecological responsibility, and post-human architectural thought, challenging inherited assumptions about the scope and function of architecture as such. Full article

Artificial intelligence (AI) has become a significant paradigm of methodology and epistemology in the social sciences. Machine learning (ML), natural language processing (NLP), and generative models enable researchers to work with big, multimodal datasets, identify complex patterns, and recreate events in the social world in ways that previously were not feasible. At the same time, these innovations also lead to ethical challenges related to algorithmic bias, black boxes, data extractivism, and reinforced structural inequalities in welfare, government services, education, and criminal justice. The article critically questions the social sciences in the light of AI on three dimensions that are inextricably linked, namely: (1) the opportunities that AI provides to social-scientific inquiry; (2) the biases and constraints generated through data, models, and institutional application; and (3) ethical pathways that are necessary for the responsible governance of AI-facilitated research and decision support. The article is based on a scoping, critical thematic review of the recent literature, and its conceptualization of AI as a socio-technical infrastructure is that it produces knowledge and, at the same time, offers power. It explains the impact AI practices have on restructuring disciplines like sociology, psychology, political science, and policy analysis, and how it blindly predicts how data practices, design choices, and governance arrangements can either preserve or destroy existing hierarchies. The paper suggests an analytical framework synthesizing AI practices, social research practices, and governance structures in ethical frameworks. It argues that the emancipatory promise of AI in the social sciences is dependent on the attainment of something beyond principle-based claims of so-called ethical AI by operational governance mechanisms that make systems visible, debatable, and responsible in their respective situations. Full article

This article presents a case study of the relationship between the scope of inquiry that classroom teachers provided to Year 4 students during inquiry-based science lessons and the learning autonomy evidenced by those students. This study investigated the practices of two primary school classroom teachers engaging in similar teaching contexts and curriculum imperatives, but each teacher provided a different scope of inquiry to students during their learning. The findings indicate a complex, non-linear relationship between the scope of inquiry provided by teachers and the student learning autonomy demonstrated. Predictably, students who experienced a narrow scope of inquiry, as provided by teachers, demonstrated a low level of learning autonomy. However, this study found that when students experienced inquiry with a broad scope provided by the teacher, they demonstrated a large range of learning autonomy, not only high levels of learning autonomy but also low levels. Moreover, when students experienced inquiry with a broad scope provided by the teacher, they manifested more diverse emotions, ranging from frustration to enjoyment, when compared with times that they experienced a narrow scope of inquiry. The findings demonstrated that students could remain on task in a broad scope of inquiry environment, but found that the minimal guidance provided was challenging. The results of the current study underscore the importance of a balance between a broader scope and a narrower scope of inquiry to optimise student learning. The non-linear relationship between the scope provided and the student level of learning autonomy experienced has ramifications for unresolved conflicts about closed vs open inquiry and for quantitative research on this complex construct. Full article

Data science has become central to contemporary social, civic, and professional life, yet its integration into initial teacher education remains fragmented and undertheorised. This paper addresses the need to support teacher educators in designing learning experiences that develop pre-service teachers, who are non-data science specialists, competence in data science. A systematic scoping review of the literature was conducted across major academic databases and complemented by an expert-informed literature identification strategy. The review examined how data science is described conceptually, how it is structured within school curricula and teacher education, and what knowledge and practices are emphasised for teachers. Findings indicate that while core processes and practices of data science, such as problem formulation, data preparation, exploratory analysis, modelling, visualisation, and ethical engagement, are widely recognised, their translation into teacher education is inconsistent and often lacks coherence. In response, the paper presents a conceptual framework designed to support pre-service teachers in engaging with the processes and practices of doing data science. The framework offers a flexible, practice-informed structure that is accessible to non-specialist teachers and aligned with pedagogical decision-making in educational settings. The paper concludes by discussing how the framework, alongside practical considerations for enactment, can support the preparation of data-literate teachers capable of fostering critical, ethical, and inquiry-based engagements with data in schools. Full article

This study explores how teachers interpret the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education) and SDG 13 (Climate Action), as pedagogical frameworks and how they translate these goals into pedagogical practice through the design of digital comics. Adopting a qualitative case study design, the research was conducted within the scope of a professional development project entitled Comics of Values and the Environment. The participants consisted of 36 in-service teachers who took part in expert-led training focusing on sustainability education and digital comics design. Data were collected through semi-structured interviews with 12 teachers, teachers’ self-produced digital comics, and researcher observations of the design process. All qualitative data were analyzed using inductive content analysis. The findings indicate that prior to the training, teachers’ understanding of the SDGs was largely superficial and remained at the level of general awareness. Following the sustainability-oriented training and design-based activities, teachers developed more concrete and pedagogically grounded interpretations of SDG 4 and SDG 13. Quality education was increasingly conceptualized as a holistic process extending beyond academic achievement to include critical thinking, creativity, multimodal literacy, and lifelong learning. Climate action was interpreted not merely as raising awareness but as improving responsibility, agency, and action-oriented learning. Teachers perceived digital comics as effective pedagogical tools for making abstract environmental issues more concrete, enhancing student engagement, and supporting inquiry-based learning. Despite challenges related to technical infrastructure, curriculum constraints, and the need to simplify complex sustainability concepts, the digital comic design process functioned as a form of professional learning. It supported teachers’ reflective thinking, creative problem-solving, and pedagogical agency. The study demonstrates that sustainability-oriented digital comics design can support the translation of global sustainability goals into meaningful classroom practices and highlights the importance of design-based professional development in education for sustainable development. Full article

Literature reviews are a cornerstone of doctoral research in general, and of economic and business research, in particular. However, the exponential growth of scientific publications has made comprehensive and transparent reviews increasingly difficult. Conventional approaches, largely based on manual searches across a small number of databases, tend to be slow, error-prone, and incomplete. As a result, they constrain the scope of inquiry and, consequently, the robustness of theory development and empirical validation. This paper proposes and analyses an information system architecture driven by research questions and keyword taxonomies to automate core tasks of the literature search phase across multiple academic databases. Focusing on the domain of corporate and municipal financial distress, the authors employ a two-stage research design. First, the theoretical analysis integrates the literature on systematic reviews, automation, and financial distress prediction to derive a set of functional and non-functional requirements. Second, the experimental analysis documents a prototype front-end application designed to accelerate the literature review. The prototype is conceptualised as a socio-technical artefact that enhances IT competences and scientific resilience by enabling more efficient, reproducible, and extensible reviews. The authors conclude by discussing the scientific, technical, professional, and societal implications of the prototype, including opportunities for intellectual-property protection and avenues for future research. Full article

The aim of this article is to identify and systematize the principal research directions in sustainable strategic management (SSM) at the intersection of eco-innovation and business performance. Despite the growing prominence of sustainability in management scholarship, systematic understanding of how SSM, eco-innovation, and business performance are connected in the academic literature remains limited. In particular, it is unclear whether this intersection constitutes a coherent research domain or instead reflects a set of loosely related and fragmented lines of inquiry. To address this gap, the study combines bibliometric analysis and science mapping of 181 Scopus-indexed publications (2006–2024) with a PRISMA-guided scoping review of five core papers that explicitly link SSM, eco-innovation, and business performance. VOSviewer was used to identify thematic clusters and structural gaps, including missing or weak linkages between eco-innovation and different dimensions of business performance. Building on these findings, the article proposes a dual-path conceptual model: (1) a mediated path in which eco-innovation functions as a transmission mechanism between SSM and multidimensional business performance, and (2) a direct path linking SSM to business performance without mediation. The model further distinguishes between internal organizational conditions, which predominantly support the direct path, and external business environment factors, which are critical in enabling the mediated path through eco-innovation. The main contributions are as follows: (a) a structured mapping of the SSM–eco-innovation research field and its emerging thematic architecture; and (b) a conceptual model specifying the dual role of eco-innovation in shaping business performance outcomes. The study also outlines implications for theory, managerial practice, and public policy, particularly in terms of how organizations and their environments influence the effectiveness of different strategic sustainability pathways. The proposed framework should be interpreted as an evidence-informed conceptual model derived from bibliometric patterns and focused qualitative synthesis, rather than as a statistically validated causal model. Full article

Digital technologies have been widely adopted in the Cultural Heritage sector over the past few decades. Many museums, galleries, historic sites, and other cultural institutions now host multimedia exhibitions or temporary installations in which technology plays a significant role in shaping the visitor experience. Among these, Extended Reality (XR) technologies, including Virtual Reality (VR) and Augmented Reality (AR), have been extensively applied and studied. Spatial Augmented Reality (SAR), a branch of AR, has also become increasingly present in cultural contexts; however, the academic literature still lacks a comprehensive and systematic review of studies addressing its use. Furthermore, various methods of inquiry and evaluation have been employed to assess SAR applications in cultural institutions, both from the perspective of visitors and from that of cultural practitioners and stakeholders. This scoping review, conducted in accordance with the PRISMA guidelines, collected a total of 47 papers that introduce the academic perspectives on SAR in the cultural domain. It reports its definitions, applications, documented benefits, and the different evaluation approaches identified in the literature. Through a snowball sampling methodology, the collection has been expanded to include 33 additional studies. After the screening process, the authors reviewed 34 papers, presenting the gaps identified in the literature and outlining suggested directions for future research. Full article

Remote Indigenous communities experience persistent inequities in access to fresh and nutritious foods due to the fragility of perishable food supply chains (PFSCs). Disruptions across procurement, transportation, storage, retail, and limited local production restrict access to perishable foods, contributing to food insecurity and diet-related health risks. This scoping literature review synthesizes evidence from 84 peer-reviewed, grey, and unpublished sources across fourteen countries to map PFSC management (PFSCM) challenges affecting food access in remote Indigenous communities worldwide and to synthesize reported practices implemented to address these challenges. PFSCM challenges were identified across all supply chain levels, and five categories of reported practices emerged: PFSC redesign strategies, forecasting and decision-support models, technological innovations, collaboration and coordination mechanisms, and targeted investments. These findings informed the development of a multi-scalar conceptual framework comprising seven interconnected PFSCM clusters that organize how reported practices are associated with multiple food access dimensions, including quantity, affordability, quality, safety, variety, and cultural acceptability. This review contributes an integrative, system-oriented synthesis of PFSCM research and provides a conceptual basis to support future scholarly inquiry, comparative inquiry, and policy-relevant discussion of food access and health equity in remote Indigenous communities. Full article

Cardiovascular disease (CVD), diabetes, and obesity pose major public health challenges in the United Arab Emirates (UAE), contributing substantially to morbidity, mortality, and healthcare expenditure. Despite progress in expanding access and service delivery, Health Quality Management (HQM) practices remain constrained. This study represents one of the first comprehensive, co-productive efforts to evaluate and strengthen HQM for CVD, diabetes and obesity in the UAE. Using a sequential, multi-phase design, it integrates evidence synthesis with the active engagement of interest groups to bridge gaps between research, policy, and practice. Phase 1 involves a scoping review to establish an evidence base on existing HQM practices and system-level challenges. Phase 2 conducts mapping and interviews with health professionals, policymakers, and patients to capture contextual insights. Phase 3 synthesizes findings to identify critical gaps, opportunities, and emerging research questions that can guide future inquiry. Phase 4 convenes consultative and consensus-building workshops to co-produce actionable recommendations and facilitate knowledge translation and exchange among health authorities, academic institutions, and other interest groups. Guided by the Institute of Medicine’s quality domains, the Donabedian model, and WHO quality indicators, this study situates HQM within the UAE’s ongoing shift toward value-based healthcare. The expected outcomes include the identification of key barriers to and facilitators of effective HQM, the formulation of context-specific recommendations to strengthen performance and coordination, production of knowledge translation outputs and the generation of new research priorities, thus contributing to achieving UAE Vision 2031 and global NCD targets. Full article

In the digital age, online industry documents have become an available and abundant source to inform qualitative health research on the commercial determinants of health (CDOH), including how corporations shape knowledge, policy, and public perception to protect business interests. This paper introduces the situational scoping method, a rigorous and transparent qualitative approach rooted in critical constructivism designed to conduct an overview of large databases of industry documents and systematically map industry responses to external events perceived as threats or opportunities. Developed through a pilot study on environmental exposures and breast cancer, using the UCSF Industry Documents Library, the method consists of three stages: (1) identification of a broad range of external events over time perceived by industries as a threat or opportunity to business interests; (2) selection of a sample of external events for further analysis; and (3) social world/arena mapping of industry responses to selected external events. Conducted by a transdisciplinary team with community partners, the method builds on and enhances traditional tobacco documents and CDOH research by integrating participatory action and collaborative analysis of digital archives. It also offers a transferable framework for examining corporate influence across sectors. This work contributes to emerging public health methodologies that confront commercial power through critical, community-engaged inquiry essential for emancipatory action. Full article

Given the apparent gap between scientific research and engineering practice, this paper tracks the dominating perspectives that have shaped the growth of hydrological research. Based on five eras, dominated with specific paradigms and/or ideologies, this paper highlights the punctuated growth of flood frequency analysis comparative to the enormous progress made in hydrological modeling can be claimed by the 20th century. The historical narrative underpinning this inquiry indicates that progress in hydrological understanding can be characterized by two contrasting claims: modeling breakthroughs and inconclusive results. Contradicting statistical assumptions, complex modeling structures, the standardization of specific techniques, and the absence of any unified physical meaning of the research results brought an apparent conflict between the scope of hydrologic research and the scope of end users, i.e., civil engineers. Some hydrologists argue that the debates associated with hydrologic progress, i.e., the evolution of statistical methods, dating back to the 1960s remain unaddressed, with each era introducing additional uncertainty, questions, and concerns. Progress, for it to happen, needs synthesis among scientists, engineers, and stakeholders. This paper concludes that, in a similar way to how physicists acknowledge the conflicts between quantum and Newtonian physics, hydrology too can benefit from acknowledging divergent principles emerging from engineering practice. While many advanced analytical tools—though varied in form—are grounded in the assumption that past data can predict future conditions, the contrasting view that past data cannot always do so represents a key philosophical foundation for resilience-based civil engineering design. Acknowledging contrasting philosophies describing the nature of reality can help illuminate the conundrum in the scope of hydrological research and can enable synthesis activities aimed at ‘putting the puzzle together’. Full article

The immense potential of artificial intelligence (AI) in academic journal publishing has significantly impacted scholarly communication between stakeholders, leading to increased research into ethical considerations for AI use in academic publishing. Due to the contextual nature of ethics and the ontological base of language as its own object of inquiry, the conceptual framework and underlying ideologies of AI ethics in linguistics deserve attention. In this study, we address the call for these ethical considerations by combining a hybrid thematic analysis (HTA) of the ethical guidelines available on 144 Social Sciences Citation Index (SSCI) linguistics journals’ and 11 corresponding publishers’ websites as of 31 October 2025, and a critical discourse analysis (CDA) case study on Language Testing, a representative journal with self-developed AI ethical guidelines. Through the HTA, we identified seven themes: accountability, authorship, citation practices, copyright, long-term governance, human agency, and transparency. The role allocation of CDA demonstrated that the AI ethical guidelines independently established by the linguistics journal expand the scope of stakeholders to include the sources of research data and technology, covering the informed consent of research participants and the responsibilities of the AI tool operators. Moreover, AI tools are given a beneficialized role, suggesting a more technology-assisted-oriented perspective and reflecting deeper trust in AI’s involvement. Through the findings, our study contributes to the broader understanding of ethical governance in relation to AI usage in discipline-based communication, highlighting the need for a more dialogic and diverse framework to share responsibility among stakeholders to promote the ethical use of AI. Full article

Background: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating “hallucinations”. Objective: To combine the deterministic framework of traditional NLP with the probabilistic capabilities of LLMs, we developed the AI Virtual Assistant (AIVA) Platform. This system utilizes a retrieval-augmented generation (RAG) architecture, integrating Gemini 2.0 Flash with a medically verified knowledge base via Google Vertex AI, to safely deliver dynamic, patient-facing postoperative guidance grounded in validated clinical content. Methods: The AIVA Platform was evaluated through 750 simulated patient interactions derived from 250 unique postoperative queries across 20 high-frequency recovery domains. Three blinded physician reviewers assessed formal system performance, evaluating classification metrics (accuracy, precision, recall, F1-score), relevance (SSI Index), completeness, and consistency (5-point Likert scale). Safety guardrails were tested with 120 out-of-scope queries and 30 emergency escalation scenarios. Additionally, groundedness, fluency, and readability were assessed using automated LLM metrics. Results: The system achieved 98.4% classification accuracy (precision 1.0, recall 0.98, F1-score 0.9899). Physician reviews showed high completeness (4.83/5), consistency (4.49/5), and relevance (SSI Index 2.68/3). Safety guardrails successfully identified 100% of out-of-scope and escalation scenarios. Groundedness evaluations demonstrated strong context precision (0.951), recall (0.910), and faithfulness (0.956), with 95.6% verification agreement. While fluency and semantic alignment were high (BERTScore F1 0.9013, ROUGE-1 0.8377), readability was 11th-grade level (Flesch–Kincaid 46.34). Conclusion: The simulated testing demonstrated strong technical accuracy, safety, and clinical relevance in simulated postoperative care. Its architecture effectively balances flexibility and safety, addressing key limitations of standalone NLP and LLMs. While readability remains a challenge, these findings establish a solid foundation, demonstrating readiness for clinical trials and real-world testing within surgical care pathways. Full article

The fast advancement of artificial intelligence presents ethical challenges that exceed the scope of traditional moral theories. This paper proposes a value-centered framework for AI ethics grounded in axiology, which distinguishes intrinsic values like dignity and fairness from instrumental ones such as accuracy and efficiency. This distinction supports ethical pluralism and contextual sensitivity. Using Multi-Criteria Decision Analysis (MCDA), the framework translates values into structured evaluations, enabling transparent trade-offs. A healthcare case study illustrates how ethical outcomes vary across physician, patient, and public health perspectives. The results highlight the limitations of single-theory approaches and emphasize the need for adaptable models that reflect diverse stakeholder values. By linking philosophical inquiry with governance initiatives like Responsible Artificial Intelligence (AI) and Digital Humanism, the framework offers actionable design criteria for inclusive and context-aware AI development. Full article