Search Results (472)

This paper examines how conversational daily deep dive news podcasts build journalistic authority and legitimacy through what we call the “journalist-as-guest” (JAG) format. Applying a deep analytical listening methodology that honors the aurality of the medium and positions listening as a core analytical method, we analyzed eight daily deep dive news podcasts from Canada and Australia, attending to how sonic elements interact with conversational performance to produce podcasting’s characteristic intimacy and parasocial listener bonds that support authority and legitimacy claims for journalism. Our findings expand on our previous identification of the JAG format as a key element of explanatory-type daily deep dive podcasts. Here, we reveal how it operates through three key mechanisms: recurring self-referential speech that reinforces journalistic cultural belonging; intentional unpacking of the reporting process to reveal behind-the-scenes work; and the careful construction of journalists as subject-matter experts. Together, these mechanisms transform performative conversation into metajournalism, creating a space in which journalistic expertise is displayed and validated through colleague-to-colleague dialogue. We term this dynamic “intimate authority.” We argue that the JAG format capitalizes on podcasting’s affordances for intimacy, parasociality, and extended metajournalistic conversation to invite audiences into the news-making process while positioning journalists as credible experts and sense-makers. In doing so, it functions as a mechanism for establishing authority and legitimacy claims in digital media environments. As daily news podcasting becomes increasingly central to remediation efforts aimed at restoring trust in journalism, both legacy and independent news podcasters appear to be counting on the JAG format as a strategic response to concurrent crises of news avoidance and relevance. Full article

This study examined how exposure to constructive audiovisual news shapes people’s perception of societal issues in the Netherlands. An online experiment was conducted among 575 participants aged 18 to 90 years old. Participants were randomly assigned to watch an audiovisual news item, either constructive or nonconstructive news, about plastic waste in the ocean or the Dutch housing market. The study investigated how these different reporting styles affected participants’ perceptions of the main message, their awareness of the seriousness of the societal issue, and their evaluation of the journalistic quality of the news report. Results showed that, contrary to critics’ concerns, constructive news did not reduce perceived problem awareness: participants across conditions reported similarly high levels of awareness regarding the seriousness of the issues presented. Perceived journalistic quality also remained high in both constructive and nonconstructive conditions, indicating that incorporating constructive elements did not compromise credibility. Furthermore, constructive news appeared to encourage a broader, more solution-oriented perspective, prompting participants to consider opportunities and future prospects. Importantly, this broader perspective did not come at the expense of perceived problem awareness or journalistic quality. Overall, the findings provide empirical support for the value of constructive journalism in the Dutch media context. Full article

The construction sector is undergoing a rapid transition toward more resilient, sustainable, and digitally connected systems, creating increasing demand for materials capable of providing functions beyond conventional structural performance. In this context, smart materials have emerged as promising solutions due to their ability to respond to mechanical, thermal, chemical, or electromagnetic stimuli through adaptive behaviors such as self-healing, structural sensing, energy regulation, vibration control, and reversible deformation. Despite growing scientific interest, available knowledge remains fragmented across specific material families and isolated application domains. Therefore, this study presents a PRISMA-based systematic review of smart materials in construction using peer-reviewed journal literature indexed in Scopus during the 2021–2026 period. The review examines the principal smart material families currently applied in construction, including self-healing concretes, self-sensing cementitious systems, Shape Memory Alloys (SMA), piezoelectric materials, phase change materials, adaptive coatings, conductive nanocomposites, and multifunctional geopolymers. Their engineering functions, structural and architectural applications, reported performance characteristics, sustainability contributions, digital integration potential, and implementation barriers are comparatively discussed and qualitatively synthesized based on the reviewed literature. The findings indicate that smart materials can improve durability, structural health monitoring, seismic resilience, thermal efficiency, lifecycle performance, and carbon reduction when properly integrated into buildings and infrastructure. However, large-scale adoption remains constrained by high initial costs, manufacturing scalability, regulatory uncertainty, long-term durability validation, and limited market confidence. The review further shows that the greatest future potential lies in combining material intelligence with IoT platforms, artificial intelligence, BIM environments, and digital twins. Overall, smart materials are positioned as strategic enablers of next-generation low-carbon, adaptive, and intelligent construction systems. Full article

This article examines the utilization of WhatsApp as an alternative communication tool for disseminating visual content among social activists during protests. While WhatsApp is typically conceptualized as an interpersonal or group messaging platform, research on its role as an infrastructure for alternative media and citizen journalism remains limited. The study focuses on the “Tikva” group, established at the onset of the public struggle against the 2023 judicial reform in Israel, which evolved into a nine-month mass protest movement described as one of the largest in the country’s history. Through qualitative thematic content analysis of videos distributed within the group, the article explores how WhatsApp functions simultaneously as a channel for digital activism and as a site of bottom-up, democratic, non-institutional news production. The findings indicate two primary trends: functionally, WhatsApp operates as a mechanism for resource mobilization, calls to action in physical and digital spaces, and the cultivation of belonging and solidarity among activists facing institutional power; in terms of content and production, the videos articulate an anti-hegemonic discourse and challenge mainstream media conventions. The analysis shows how these videos dismantle delegitimizing frames and construct a counter-narrative depicting protesters as citizens defending democracy, thereby sustaining the protest movement’s momentum. Full article

This study addresses the fragmented understanding of tourist responses to AI-generated content (AIGC) in tourism and hospitality by developing a process-oriented systematic review. While prior studies have examined AIGC-related trust, authenticity, credibility, and adoption, these constructs have often been treated separately, limiting theoretical understanding of how tourists evaluate and rely on AI-generated tourism content. Based on a systematic review of 98 peer-reviewed journal articles retrieved from Scopus and the Web of Science Core Collection and published between January 2023 and March 2026, this study synthesizes the literature around four connected stages: perceived AIGC attributes, evaluative judgments, trust calibration, and behavioral responses. The findings show that tourist responses to AIGC are not direct reactions to technological exposure, but emerge through a layered process in which tourists assess content quality, credibility, authenticity, and contextual appropriateness before deciding whether and how far to rely on AI-generated outputs. The review contributes by reconceptualizing trust as a dynamic calibration mechanism, distinguishing authenticity from credibility and trust, and identifying reliance as a key bridge between evaluation and behavior. The study offers a process-oriented framework and a future research agenda for advancing more theoretically integrated and context-sensitive research on AIGC in sustainable digital tourism. By clarifying how tourists evaluate, trust, verify, and rely on AI-generated tourism content, the review contributes to sustainable tourism development by highlighting the conditions under which AIGC can support more responsible, transparent, and human-centered tourism communication. These insights are relevant to destination sustainability because trustworthy and context-sensitive AIGC can improve information quality, reduce misleading representations, and support more informed tourist decision-making. Full article

Very tall slender structures are constructions that, due to their exceptional structural characteristics, are most exposed to environmental factors, especially wind and uneven sunlight. We refer specifically to smoke chimneys over 200 m and tall television towers, which, due to their truncated conical structure, exhibit behavior different from residential-type structures. Environmental stresses manifest as forces that can induce reversible tilts and oscillations—when their value significantly exceeds design values, they can cause damage or even destruction of the construction. Monitoring the preservation of structural integrity under the influence of environmental factors—a fundamental component of Structural Health Monitoring (SHM)—is essential for safety and maintenance. In the SHM of very tall slender structures, many studies employ various theories, methodologies, and technologies that have advanced rapidly due to the expansion of information technology. The objective of this study is to identify areas lacking research in the existing literature regarding environmental factors influencing the reversible displacement of very tall slender structures, along with the analysis of techniques and technologies used for monitoring these structures. To achieve this objective, the most critical environmental factors and technologies, especially sensor-based ones, were identified through a systematic search of the most popular databases. Subsequently, the study employs a bibliometric analysis, exploring challenges and prospective research areas reflected in the specialized literature. An extensive analysis of the State-of-the-Art on the subject in the specialized literature—particularly that published by the most prestigious journals in the field—was conducted. The findings indicate a lack of scientific investigations on environmental factors influencing SHM of very tall slender structures, especially studies on the effect of uneven sunlight on structures. The research provides a comprehensive understanding of SHM of very tall slender structures and has practical implications for developing effective monitoring methodologies. Full article

This study examines how early Soviet culture sought to render time perceptible, governable, and politically productive by translating Futurist “chrontology” into socialist “chronization.” Taking Aleksandr Kusikov’s 1922 polemic on the Berlin journal Veshch (Thing)—edited by Ilya Ehrenburg and El Lissitzky—as its point of departure, it reconstructs a “third-way” Futurism shaped by postrevolutionary transit and exile, in which anticipatory futurity was inseparable from an intensified orientation toward materiality, technique, and constructive making. Against the backdrop of modern heterochrony—where “times” proliferate and synchronization remains contested—the essay traces how Soviet actors devised new units, comparisons, and pedagogies for living in a temporally unstable present. Two case studies anchor the argument. First, the League of Time (1923–1925) promoted multiscalar time-management practices through campaigns, memos, and didactic graphics that treated clocks, routines, and efficiency as instruments of socialist subject-formation. Second, illustrated children’s books translated variegated temporal sensibilities into a pedagogy of images, making futurity legible via “thingful” traces that recoded the past as a catalogue of obsolete objects. Chronization thus appears as an ambitious cultural technology for producing a planned, dynamic, and productive socialist way of being. By foregrounding work, visualization, and material comparison, these projects converted temporal uncertainty into actionable, collective everyday socialist practice. Full article

Automated linkage between geoscientific literature and datasets is essential for improving data reuse, reproducibility, and knowledge discovery, yet existing methods often struggle with implicit dataset references, heterogeneous spatial–temporal expressions, and inconsistent naming conventions. To address this problem, we propose a literature–data linkage framework that integrates candidate retrieval, large language model (LLM)-based structured extraction, normalization, and knowledge graph construction. The framework first identifies candidate fragments through BM25-based retrieval, regex filtering, and whitelist-assisted scoring, and then applies schema-constrained prompting to extract dataset names and key attributes, including temporal coverage, spatial scope, resolution, provider, and role. The extracted results are subsequently normalized to canonical forms and ingested into a Neo4j-based knowledge graph linking articles, datasets, institutions, and regions. Experiments on a cross-journal benchmark show that the proposed framework achieves 93.79% precision, 90.66% recall, and 92.20% F1-score. Comparative experiments across multiple LLM backbones further indicate that the framework remains effective across both proprietary and open-source models, while ablation results confirm that candidate retrieval and normalization are the two most influential components for balanced extraction performance. The resulting knowledge graph provides a structured representation of literature–data linkages and supports exploration of dataset reuse patterns, provenance relations, and cross-document connections. These results demonstrate that carefully constrained LLM extraction, combined with retrieval and normalization, provides a robust and interpretable pathway for transforming unstructured geoscientific literature into structured and reusable knowledge. Full article

Topic modeling is one of the most widely used Natural Language Processing models in business fields. In this survey, by collecting and reviewing 140 topic modeling-related articles published in 40 finance and related business journals, I document the trend of topic modeling across journals and time, review the main algorithms used in the literature, and organize the evidence by research areas, research methodologies, and data sources. The survey shows that Latent Dirichlet Allocation is the dominant approach especially in early studies, but newer variants, such as supervised LDA, correlated topic modeling, sentence-level models, and structural topic models, are being adopted when researchers need better model performances under specific cases. Recent work increasingly uses topic-based methods to summarize documents, construct new measures, classify disclosures, and compare text information from firms, market participants, and policymakers. Though topic modeling algorithms are powerful, challenges such as noisy documents, topic labeling, and Blackbox issues still exist. Overall, topic modeling has moved from a supplementary textual analysis tool to a main research tool in finance research, and topic modeling will accelerate the development of finance research in the near future. Full article

Mistreatment is a pervasive and normalized feature of medical culture. In medical residencies, it functions as a structural rite of passage that shapes professional socialization. While the prevalence of mistreatment is documented, there is a lack of qualitative research exploring its role as a mechanism of identity construction. The aim of this study was to understand the experiences of mistreatment among internal medicine residents in Medellín, Colombia, through the lens of ritual theory and symbolic violence. A particularistic ethnographic study was conducted with 12 residents selected via theoretical sampling. Data were collected through semi-structured interviews and a reflexive field journal. Rigor was ensured using investigator triangulation and analytical bracketing to manage researchers’ biases. The training process follows a three-stage rite. (1) Separation: Symbolic violence and social pressure to specialize frame general medicine as “mediocre,” turning admission into a “battlefield” where self-worth is tied to success. (2) Marginalization (Liminality): Residents endure systemic mistreatment, including sleep deprivation (3.5 h rest cycles), public ridicule (“pimping”), and physical/verbal abuse (e.g., being hit with stethoscopes or called “testicles/jerks”). This stage is governed by a “purificatory logic” where suffering is internalized as a meritocratic requirement. This leads to high morbidity, with clinical diagnoses of anxiety and depression. (3) Integration (Postliminality): Professional autonomy and financial stability act as a “redemption” that justifies past suffering. Mistreatment is not an isolated interpersonal issue but a structurally embedded ritual and a core element of the hidden curriculum. It reinforces toxic hierarchies and a “tyranny of merit” that obscures structural barriers. These findings offer analytically transferable insights for global medical education, calling for a deconstruction of ritualized violence to foster more humanistic training environments. Full article

(1) Background: Knowledge diffusion of academic publications has become a crucial indicator of research impact in the context of open science, yet its influencing factors and underlying mechanisms remain insufficiently studied. (2) Methods: Based on literature research, this study constructed a multi-dimensional factor system consisting of 17 factors. The DEMATEL method was used to identify the key influencing factors. The mean and standard deviation of the comprehensive influence matrix were taken as the threshold (λ = 0.87) to filter important relationships and establish an adjacency matrix. The ISM method was used to explore the hierarchical relationship of the influencing factors, and finally, strategies were proposed. (3) Results: The results show that the journal characteristics have the highest centrality (30.292), the author characteristics have the strongest causal effect (0.686), and the language characteristics are at the bottom of the influence factor hierarchical structure model, possessing certain driving force and being closely related to multiple factors. (4) Conclusions: Effective enhancement of knowledge diffusion requires coordinated optimization across language expression, core research elements, methodological execution, dissemination channels, and presentation formats, providing theoretical and practical implications for academic evaluation and research management. Full article

(1) Background: Climate change and inequality are topics of major interest in Mediterranean Archaeology. However, comparatively less attention has been dedicated to how these themes are interlinked in the literature. No scoping review has ever addressed this issue. This study aims to identify major research trends on inequality and climate change in the Mediterranean c. 4000 BC–AD 500. It also pinpoints current research gaps on the topic and nascent areas of enquiry. (2) Method: We performed a scoping review on JSTOR, Scopus, Google Scholar and PubMed in December 2025–January 2026. A modified version of the PRISMA-ScR protocol was followed. We sampled journal articles, book chapters, edited volumes and monographs published between 2015 and 2025 which matched the search and inclusion criteria. Additional searches were done on Google Scholar in February 2026 to expand upon emerging research trends relevant to our topic but largely absent from the scoping review. We manually extracted, charted, analysed and synthesised the data. (3) Results: A total of 154 studies were eligible for the scoping review. We identified six research trends prominent in the sampled literature: 1. the rise and fall of world systems, macroscale causal links, and collapse research; 2. inequality, subalternity, and marginality; 3. agriculture, crops, and diet; 4. natural resource management, and water supply; 5. epistemology and methodology; and 6. natural archives and climate proxy datasets. We also recognised the following research gaps or topics that were comparatively less addressed: collapse research applied to the microscale level and marginalised communities; isotope analysis applied to both climate change and inequality in the same study; biomedical approaches applied to both climate change and inequality in the same study; social marginality as a complex construct in human–climate interactions; and the environmental and climate dimensions of the early Roman expansion, especially regarding marginality and the microscale. Finally, we identified artificial intelligence (AI), Big Data, environmental and climate activism, and the perception of climate hazards by subaltern communities as nascent topics of interest that might rise to prominence in the future. (4) Conclusions: We identified major research trends and gaps on climate change and inequality in the ancient Mediterranean in literature published 2015–2025. We also recognised nascent or unexplored topics. The review is intended as a benchmark for developing novel research on the cutting-edge of Mediterranean Archaeology. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

Background: Leadership in sustainability-oriented inter-organizational networks is increasingly enacted through governance-related practices rather than firm-centric or individualized constructs, reflecting distributed authority, shared accountability, and plural sustainability objectives. Yet scholarship remains conceptually fragmented across adjacent constructs such as orchestration, meta-governance, and brokerage. Objective: This systematic review synthesizes how leadership is conceptualized and enacted through governance mechanisms in inter-organizational networks pursuing sustainability goals. Methods: Peer-reviewed journal articles in English were included; non-peer-reviewed publication types and studies lacking substantive inter-organizational and leadership/governance relevance were excluded. Structured searches were conducted in Scopus and Web of Science Core Collection (last searched 11 February 2026). Results were synthesized through qualitative narrative synthesis using iterative thematic coding and narrative integration. Risk of bias was not formally assessed because the review aimed at conceptual mechanism integration rather than effect estimation; interpretive adequacy safeguards guided inclusion and synthesis. Results: Thirty-one peer-reviewed journal articles were included. Across the corpus, leadership is primarily theorized as (i) orchestration and meta-governance; (ii) governance mechanisms as the formal and informal infrastructure enabling and constraining network leadership; and (iii) brokerage and boundary-spanning practices that align actors and mediate institutional tensions. These dimensions operate as mutually reinforcing layers of coordination capacity, shaping how sustainability trade-offs become governable in the absence of hierarchy. Limitations: Evidence is limited by database-only searching, English-language restriction, and the absence of a formal risk-of-bias appraisal; findings are therefore interpretive and mechanism-oriented rather than effect-based. Conclusions: The review advances a conceptual reframing: leadership in sustainability-oriented inter-organizational networks is best understood not as an actor property but as a systemic coordination capacity embedded in governance architecture. By articulating meta-governance as a design layer, orchestration as a coordination layer, and brokerage as a translation and legitimacy layer, the study develops a multilevel analytical model integrating leadership and governance at the network level, with implications for innovation ecosystems, strategic collaboration, and sustainability transitions. Full article

Marginal land refers to areas where environmental constraints prevent cost-effective production; driven by global energy and food security strategies and greenhouse gas reduction targets, its potential productivity has garnered significant attention. However, macro-level and data-driven knowledge graph analyses remain scarce. Therefore, this study employed VOSviewer, Bibliometrix R, and CiteSpace to conduct a bibliometric analysis of 2535 Web of Science Core Collection documents (1995 to 2025). Findings reveal that: (1) The research is split into four stages—early exploration, fluctuating development, rapid growth, and stable output, peaking at 191 papers in 2021. (2) The collaboration of authors and institutions has reached an initial scale but remains relatively weak, with Lewandowski and the Chinese Academy of Sciences occupying the core of the co-authorship network. The United States (N = 501) ranks as the top-producing country. France (MCP% = 56.8%) leads in international collaboration intensity, and US—China cooperation is the most frequent. (3) Journals primarily focus on environmental science and agronomy, with Global Change Biology Bioenergy leading with 109 publications. (4) Through consolidating core keywords like “marginal lands,” “biomass,” and “productivity” into four perspectives: technological/model interventions, farmer behavioral choices, natural baseline conditions, and theoretical understanding, this study constructs a comprehensive framework to help research become sustainable. Full article