Abstract:

Research integrity is currently besieged by a surge in synthetic manuscripts. A forensic workflow is operationalized herein to isolate and quantify ``computer-aided'' misconduct within the global scholarly record. A corpus of \( N=3,974 \) retracted DOIs sourced from the Retraction Watch Database was analyzed, with records cross-linked to institutional metadata via the OpenAlex API. Through the application of fractional attribution modeling and the calculation of Shannon entropy (\( H \)) for retraction rationales, a distinct geographic schism in fraud typologies was identified. High-output hubs, specifically China and India, exhibit high reason entropy (\( H > 4.2 \)), where ``Computer-Aided Content'' frequently clusters with established ``Paper Mill'' signatures. These AI-driven retractions exhibit a compressed median Time-to-Retraction (TTR) of \( \sim \)600 days, nearly twice as fast as the \( 1,300 \)+ day latencies observed in the US and Japan---where retractions remain skewed toward complex image and data manipulation. The data suggests that while traditional fraud has not been replaced by generative AI, it has been effectively industrialized. It is concluded that current post-publication filters fail to keep pace with the near-zero marginal cost of synthetic content, necessitating a shift toward provenance-based verification.

Abstract: Generative artificial intelligence (GenAI) is a paradigm shift that redefines knowledge retrieval, development, communication, and verification. However, GenAI’s capabilities for mimicking the work of human scholars have advanced, generating content indistinguishable from that of human authors, making pre-publication editing and peer review challenging. Through an extensive bibliographic exploration of the Scopus and HeinOnline databases, we investigated academic, ethical, and legal attitudes toward GenAI and scientific authorship. A backward reference search was conducted, beginning with bibliographic evidence published from 2022 to 2025, to identify earlier contributions that demonstrate stakeholders' positions. Index keyword co-occurrence analysis was performed to identify trends and attitudes among the scientific and legal professional communities. It is well recognized that GenAI impacts the traditional ideas and practices of authorship, creativity, ownership, and copyright, but accountability and responsibility remain with the authors. Although the need for reforming guidelines and laws related to these subjects is unanimously recognized, academic scholars tend to debate theoretical and doctrinal subjects, while legal professionals focus on the deliberate misuse of GenAI, legal schemes, ethical compliance, verification of the origin of content, and unauthorized use of resources protected by proprietary rights. The ongoing technological developments in GenAI powerfully shape opinions and drive new ideas for the scientific and legal community.

Abstract: This paper reviews research literature on Diamond Open Access (DOA) journals – sometimes also called Platinum Open Access – that was produced after this journal segment started to become a priority in European research policy around 2020. It con-textualizes the current science policy debate, critically examines different understand-ings of DOA, and reviews studies on the role of such journals in scholarly communica-tion. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyond understanding this particular journal segment, as it also challenges established views of the global system of scholarly communication.

Abstract: Well-cited articles identify Google Scholar as a sufficiently lacking database to evaluate it as supplementary regarding the preferred reporting items for systematic reviews and meta-analyses: PRISMA. Subsequent author systematic review searches have accepted this relegation of Google Scholar to supplementary status without examination. This study questions this acceptance by (1) revealing the type of difficulties with Google Scholar identified in these well-cited publications compared with PRISMA guidelines, and (2) examining several PRISMA scoping review primary database searches performed by this author since 2023 for the adequacy of Google Scholar results compared with them. The results reveal that the reasons for considering Google Scholar a supplementary database regarding PRISMA status are not convincing, as they are unrelated to PRISMA guidelines for systematic reviews. Additionally, Google Scholar was the source of the most relevant included studies for the majority of this author’s post-2023 scoping reviews. These results demonstrate that the accepted advice to authors that Google Scholar should be a supplementary database is unsupported. Regarding PRISMA guidelines, based on the results of this original research, there should be immediate reconsideration of Google Scholar's status for acceptance as a primary database.

Abstract: Artificial Intelligence (AI) has gained prominence on sustainability agendas while raising ethical, social, and environmental challenges. This study synthesizes evidence and maps the scientific production on Human-Centered AI (HCAI) at the interface with the Sustainable Development Goals (SDGs) for 2020–2024. Searches in Scopus and Web of Science (Boolean operators; thematic and temporal filters), followed by deduplication, yielded 265 articles, which were analyzed with Bibliometrix/Biblioshiny and VOSviewer to generate term co-occurrence maps, collaboration networks, and bibliographic coupling. The results indicate accelerated growth and diffusion of the topic, with journals such as Sustainability, IEEE Access, and Applied Sciences-Basel standing out. Three interdependent axes were identified: (i) technical performance, with emphasis on machine learning and deep learning; (ii) explainability and hu-man-centeredness (XAI, ethics, and algorithmic governance); and (iii) so-cio-environmental applications oriented toward the SDGs. Underrepresentation of the Global South, particularly Brazil, was observed. It is concluded that HCAI is consoli-dating as an emerging interdisciplinary field with potential to accelerate the SDGs, although there remains a need to integrate ethical, regional, and impact-assessment dimensions more systematically to achieve global targets effectively.

Abstract: The effect of climate change has negatively impacted agricultural production and food security in the Sub-Saharan Africa region, particularly among smallholder farmers in Southern Africa. These challenges have been exacerbated the limitations in traditional agricultural communication systems and extension services. However, global innovations in digital technologies for communication provide opportunities for improving climate change information. This paper explores communication climate communication challenges among selected countries in the Southern African region and suggests how emerging digital technologies may be harnessed for climate communication among smallholder farmers. The review shows that while nations had embraced traditional digital technologies for communicating climate information, few innovations supported by emerging technologies have been developed to support climate communication among farmers. The review showed that farmers have continued to face several challenges in accessing and disseminating climate information. The review also shows that the few innovations that have been developed have remained at incubation level and a few of this innovation have been commercialized to support climate communication among smallholder farmers. The study therefore recommended further research on the integration of emerging technologies such as artificial intelligence, block chain technology and data analytics in climate change communication. The study further recommends policy interventions on improving accessibility of these emerging technologies among smallholder farmers.

Abstract: This study anchors its analysis on the controversy surrounding Shanghai Maritime University's “First-Class Undergraduate Programs” initiative in China. It aims to validate whether AI-enhanced open-source intelligence (OSINT) evidence governance can reconstruct auditable evidence chains and quantify narrative discrepancies in education-related public incidents, while extrapolating governance performance to multidimensional national security risks. Methodologically, we collected university announcements, mainstream media reports, platform-accessible pages, and judicial leads within a unified time window to construct a “source-time-version” evidence graph. We introduced multilingual NLI and sentence vectors to measure narrative consistency, combined with JSD to monitor monitoring scope drift. We employed temporal propagation networks and DTW to measure the temporal misalignment between “post deletion/restriction” and public opinion peaks. Further, we mapped “evidence governance/information control opacity” to four normalized risk dimensions—academic integrity, rule of law and institutional trust, ideological security, and social stability—using structural causal models, providing 95% confidence intervals. Results indicate: The foundational evidence chain can be robustly reconstructed, yet significant narrative gaps exist regarding “whether ‘7 categories, 40 items’ of fraud occurred” and “whether platform actions were implemented/compliant.” Communication peaks typically precede search peaks by approximately one cycle (moderate DTW misalignment), with public nodes consistently holding the highest intermediary scores. Among the four risk dimensions, “academic integrity” and “ideological security” exhibit relatively higher levels. Conclusions indicate that earlier, auditable evidence disclosure (versioned notifications, third-party verification, platform log retention) simultaneously reduces all four risk dimensions and shortens the public opinion chain, whereas declarative statements alone fail to resolve disputes. AI-enhanced OSINT provides a reusable methodological pathway for evidence governance in educational settings and national security assessments.

Abstract: Kazi Nazrul Islam, referred as the "Rebel Poet," is a significant figure in modern Bengali literature. Moreover, he is recognized for his contributions not only to Bengali literature, but also to music, and political ideology. His work advocates for equality, secularism, anti-colonialism, humanism, social justice, communal cohesion, and educational reform. This research is an attempt to elucidate his impact in contemporary global contexts using systematic review and meta-analysis. This study performed an extensive literature review using prominent global indexing and abstracting databases, including Web of Science, Lens.org, Scopus, Dimensions, and Google Scholar. The databases were queried using the phrase "Kazi Nazrul Islam," and data from Google Scholar was extracted utilizing the software "Publish or Perish" (PoP). The data was stored in Microsoft Excel, consolidated, and duplicate entries were eliminated. The final Excel sheet had 148 records, selected for conclusive examination, guaranteeing thorough representation of academic literature across many disciplines. This study has observed that research on Kazi Nazrul Islam has increased in recent years. The majority of papers not cited, because social science articles garnering fewer citations than those in the natural sciences. Finally, six major topics are delineated: Life Poetry and Politics, Global Influence, Stylometry and Authorship in Bengali Literature, Translation Studies, Tagore and Political Thought, and Post-colonialism in Bengali Literature.

Abstract: Information theory, originally rooted in thermodynamics, is utilized in scientometrics to quantify the diversity and heterogeneity of knowledge combinations. This study analyzes a large-scale journal citation network to introduce and empirically validate a fundamental principle of reference behavior, which we term "Mediating Similarity." We posit that a journal's reference distribution (the knowledge it cites) acts as a cognitive bridge between its own citation distribution (its identity in the scientific landscape) and the overall scientific content distribution (the broader knowledge environment). This phenomenon is captured by the Kullback-Leibler (KL) divergence inequality: the sum of the distances from a journal to its references and from its references to the global landscape is less than the direct distance from the journal to the global landscape. Our experimental findings provide robust, multi-level evidence for this principle. First, we demonstrate the universality of the phenomenon, showing that for all 19,129 journals in our dataset, the mediated KL divergence path is consistently shorter than the direct path. Second, we conducted two perturbation experiments on the top 500 journals ranked by the SJR indicator. Based on our finding that real references are mostly contained within the closest journals as measured by KL divergence, we created a high-relevance candidate pool for each journal, consisting of its real references plus twice the actual number of citations closest un-cited journals. In a global resampling test, we found that the actual reference portfolio exhibited a lower "cognitive energy" (sum of KL divergences) than 99% of 1,000 randomly assembled portfolios from the candidate pool averagely.This indicates that citation is a holistic process that selects for a synergistically optimal combination of references. In a local perturbation test where 10% of real references were swapped, the actual portfolio still outperformed the majority of 1,000 perturbed variations. This suggests that the real-world reference selection process, while driven by an optimization principle, operates as a robust "satisficing" strategy within the constraints of the scientific discovery process. Collectively, these findings reveal that reference behavior is a strategic process. Journals selectively curate references to construct an optimal cognitive path, efficiently shortening the distance between their field and the broader scientific environment.

Abstract: Artificial intelligence (AI) is rapidly reshaping academic research, offering powerful tools for literature review, data analysis, and knowledge synthesis while raising pressing concerns about reliability, integrity, and ethics. This paper provides an integrative review combining thematic syntheses of scholarly literature with a comparative case analysis of five representative AI tools—Storm, AnswerThis, Coral AI, NotebookLM, and Zotero. The analysis highlights AI’s capacity to accelerate research efficiency, broaden access to knowledge, and support collaborative workflows, while also surfacing risks such as fab-ricated or biased outputs, shallow synthesis, and threats to data privacy. Cross-cutting themes emphasize the importance of transparency, provenance, and human oversight, particularly through practices such as separating generation from validation, and dis-closing AI involvement in scholarly outputs. The paper contributes a unified framework situating benefits, risks, and ethics in academic AI use; practical illustrations of hu-man–AI complementarity across diverse tools; policy-relevant insights for governing high- versus low-stakes research applications. The findings converge on a central princi-ple: AI is not a replacement for human scholarship, but a collaborative partner whose outputs require verification, contextualization, and ethical governance. By adopting risk-sensitive, transparent practices, the research community can move beyond polarized debates toward a pragmatic model of responsible human–AI synergy.

Abstract: The spread of fake news and misinformation poses significant challenges to the integrity of information ecosystems, undermining public trust. Libraries, traditionally trusted sources of credible information, are in a unique position to address this issue through the integration of artificial intelligence (AI). This paper explores the potential of AI to detect misinformation and enhance critical information literacy. AI technologies like natural language processing and machine learning can analyze text patterns, verify sources, and identify fake news at scale. Tools such as fact-checking algorithms and real-time content monitoring systems can help librarians curate reliable resources and guide users in distinguishing credible information from misinformation. AI can also be employed to promote critical information literacy through personalized educational experiences, including chatbots and virtual assistants that offer on-demand guidance on evaluating information. Ethical considerations play a crucial role in AI implementation. The paper addresses concerns over biases in AI algorithms, data privacy, and the ethics of automated decision-making. Strategies for mitigating these risks include prioritizing transparency, accountability, and user-centered design. By upholding ethical standards, libraries can align AI use with their core mission of serving the public good. The study also highlights the practical challenges libraries face in adopting AI, such as resource constraints, staff training, and system integration. Case studies from pioneering institutions offer insights into overcoming these barriers. Libraries can implement AI to combat misinformation and foster critical information literacy while maintaining ethical principles. This approach strengthens libraries' roles in ensuring informed, equitable access to information and positions them as key players in the fight against fake news.

Abstract: This study employs an industry-specific patent classification methodology (ISPCM) and complex network analysis across temporal, industrial, and spatial dimensions to examine China’s new energy vehicle (NEV) patent collaboration network and uncover the mechanisms underlying China’s global ascendancy in the NEV sector. The results demonstrate the effectiveness of the ISPCM and reveal a three-phase growth pattern driven by policy initiatives and market expansion. Domestic entities dominate the patent landscape, with a noticeable shift from invention patents to utility model patents, reflecting a focus on application-oriented innovation. The collaboration network exhibits small-world and scale-free properties, forming an oligopolistic structure where state-owned enterprises (SOEs) act as "innovation orchestrators," while private firms concentrate on specialized R&D. Across the industrial chain, the component segment serves as a hub, the complete vehicle segment remains relatively isolated, and the aftermarket clusters around battery recycling. A clear divide between domestic and foreign entities suggests potential decoupling risks. The findings reveal a dual-circulation innovation model that combines state-led coordinated research with market-driven independent research, offering valuable insights for sustainable industrial transformation.

Abstract: Bibliometric indicators play a key role in assessing research performance at individual, departmental, and institutional levels, influencing both funding allocation, and university rankings. However, despite their widespread use, bibliometrics are often applied indiscriminately and without discrimination, overlooking contextual factors that affect research productivity. This research investigates how gender, academic discipline, institutional location, and academic rank influence bibliometric outcomes within the Greek Higher Education system. A dataset of 2015 faculty profiles members from 18 universities and 92 departments was collected and analyzed using data from Google Scholar and Scopus. The findings reveal significant disparities in publication and citation metrics: female researchers, faculty in peripheral institutions, and those in specific disciplines (such as humanities) tend to score lower values across several indicators. These inequalities underscore the risks of applying one-size-fits-all evaluation models in performance-based research funding systems. The paper moves beyond one-size-fits-all perspective and proposes that bibliometric evaluations should be context-sensitive and grounded in discipline and rank-specific benchmarks. By establishing more refined and realistic expectations for researcher productivity, institutions and policymakers can use bibliometrics as a constructive tool for strategic research planning and fair resource allocation, rather than as a mechanism that reinforces the existing biases. The study also contributes to ongoing international discussions on the responsible use of research metrics in higher education policy.

Abstract: This article presents a comprehensive bibliometric analysis of scientific production in Central America between 1996 and 2023, based on data indexed in the Scopus database. The study frames the investigation within the broader context of scientific visibility and regional development, aiming to evaluate trends in research productivity, thematic specialization, and collaboration patterns across Belize, Guatemala, Honduras, El Salvador, Nicaragua, Costa Rica, and Panama. A total of 35,146 articles authored by researchers affiliated with institutions in Central America were analyzed using indicators such as publication volume, co-authorship networks, subject areas, and journal impact. The findings reveal a consistent annual growth rate of 7%, yet with marked disparities among countries. Costa Rica, Panama, and Guatemala account for 82% of the total scientific output, while the remaining countries contribute only 18%. Research activity is primarily concentrated in the medical, agricultural, biological, and environmental sciences. Over 80% of publications involve international collaboration, predominantly with the United States, Spain, and Mexico, whereas intraregional cooperation remains limited. The analysis underscores the region’s reliance on global research networks and reveals persistent internal asymmetries in scientific development. The results suggest the need for increased investment in national research systems, stronger regional collaboration, and targeted strategies to balance scientific production across countries.

Abstract: Academic publishing is central to scholarly knowledge validation and dissemination, yet persistent issues such as editorial gatekeeping, prestige-driven evaluation metrics, and global inequities in knowledge access continue to shape the field. These challenges have significant implications for research integrity, inclusivity, and the equitable dissemination of educational scholarship. The conversation around these topics is on-going, but little is known about how these discussions have evolved within the scholarly literature. This study presents a three-fold bibliometric analysis of scholarly literature on academic publishing reform published between 2000 and 2025. The analysis examines the literature landscape on the topics of (1) Gatekeeping and Editorial Bias, (2) Prestige-driven Metrics and Research Assessment, and (3) Barrier and Equity Issues in Research Accessibility. Using structured searches in Scopus and bibliometric analysis techniques (i.e., performance and conceptual analysis), this study identifies key publication trends and thematic patterns within each area. Time-sliced analyses further explore how discourse has shifted in response to major milestones such as the San Francisco Declaration on research assessment and the rise of the sci-hub shadow library. By providing a comprehensive overview of how these critical conversations have evolved across global scholarship, this study contributes to a deeper understanding of the dynamics, thereby shaping the future of scholarly communication and offering insights to inform ongoing reform efforts for educators, researchers, and policymakers to foster a more inclusive, transparent, and equitable academic publishing landscape.

Abstract: The Central Library of Rajarambapu Institute of Technology significantly strengthens academic excellence and supports research activities, directly contributing to the institute's reputation and rankings. This study looks into the impact of the RIT's Central Library on raising institutional rankings through a detailed case study. It explores the library's role in making access to high-quality resources easier, supporting faculty and students in their research, and opening the door for interdisciplinary collaborations. Key metrics, such as library services, resources, utilisation, various activities, and MOUs, are analysed for user satisfaction to improve research, and library users play an important role in institutional ranking systems. The study also highlights best practices, including digital resource integration, library activities, innovative information literacy programs, Hands-on Programs for students and staff and the adoption of advanced technologies like artificial intelligence for personalized learning experiences. The findings underscore the RIT Central Library's strategic role in improving research productivity, upgrading research networks, and improving the overall institutional rankings. This paper provides actionable recommendations for library administrators and institutional policymakers to optimize library services as a tool for institutional growth and competitive advantage.

Abstract: How to enhance the efficiency and quality of science communication, especially in the context of the continuous threat of global infectious diseases and the resurgence of the COVID-19 pandemic, has become a key issue in current publication evaluation and information governance. This paper conducts a systematic analysis of highly concerned COVID-19 research papers on social media by integrating methods such as bibliometrics, altmetrics, and text mining, revealing their dissemination characteristics, influence, and thematic evolution trends, and providing reference materials for the dissemination and evaluation of scientific information in the event of a possible major crisis in the future. The primary data source is the Dimensions COVID-19 literature database. The Altmetric Attention Score (AAS), an indicator of online impact, is used as the measure of social media attention. The research process begins by ranking papers in descending order based on their AAS scores and selecting the top 6,000 articles for analysis. Empirical methods such as descriptive statistics, correlation analysis, and Latent Dirichlet Allocation (LDA) topic modeling are then applied. The findings are as follows: (1) The top contributing countries of high-AAS papers are identified, along with the key research contents of the top ten papers and the major platforms on which these papers are shared. Differences in influence are also discussed in terms of author, institution, and country-level collaboration. (2) There is a positive correlation between citation counts, AAS, and journal H-index. Both the journal H-index and SJR (SCImago Journal Rank) are positively correlated with AAS, but AAS appears to have a stronger effect on the journal H-index than on the SJR. (3) Text mining results show that the 6,000 high-AAS papers cluster into four main research topics, with identifiable temporal evolution patterns. This study is based on a large sample of COVID-19 papers with high social media attention, integrating Altmetrics , traditional bibliometrics and text mining methods, and has achieved substantial expansion in research scale, analysis dimension and subject depth compared with existing research, providing a new paradigm for public health crisis communication research. The research results can provide a reference for expanding the theory and practice of public health bibliometrics.

Abstract: The current academic publishing system is increasingly defined by exclusionary gatekeeping, prestige-driven metrics, and restricted access, all of which undermine equity, innovation, and scholarly integrity. This position paper critiques these systemic issues and proposes a reform framework grounded in transparency, inclusivity, and sustainability. Key recommendations include mentorship-centered editorial models, post-publication peer review, diversified evaluation criteria, and the development of community-led open access platforms. The paper also anticipates common concerns, such as fears of declining quality and resistance from entrenched stakeholders, and offers counterarguments grounded in evidence and practice. The paper concludes with a call to action for researchers, editors, academic institutions, funding agencies, and policymakers to collaboratively transform the publishing landscape, ensuring that the production and dissemination of knowledge remains a public good.

Abstract: Purpose – Explaining the role of environmental artificial intelligence in improving the management of public libraries and enhancing environmental sustainability in them. And analyzing the factors affecting and challenges of using environmental artificial intelligence to achieve environmental goals. Design/methodology/approach – analyzed the role of environmental artificial intelligence (AI) applications in public libraries in the United Arab Emirates. The study posed 43 questions to the 31 public libraries in the UAE, examining the current level of knowledge among Emirati public library specialists about environmental AI, practices within Emirati public libraries to achieve environmental sustainability, and the challenges of adopting environmentally smart applications in Emirati public libraries. Findings – A significant portion of staff in Emirati public libraries (52%) have either limited of environmental sustainability principles, highlighting a substantial gap in environmental awareness and training within these libraries. While 68% of library staff recognize the importance of environmental sustainability, 52% of libraries do not implement sustainability practices at all, often due to a lack of institutional support or sufficient awareness. Less than half (48%) of Emirati public libraries have not used environmental artificial intelligence applications or were unaware of them. Originality/value – Environmental AI in public libraries is an emerging area of research that bridges library science and environmental health, making it a potential research area. Contributions to the research area help public libraries to make the most of environmental AI applications and raise awareness of these new technologies. Research limitations/implications - The study not only expands the knowledge base on environmental sustainability, but also provides an understanding of the development of environmental AI to provide advanced research support in this field.

Abstract: This article investigates the architecture and institutional distribution of policy tools supporting Open Science (OS) in China. Based on a corpus of 199 policy documents comprising 25,885 policy statements, we apply an AI-assisted classification to analyze how the Chinese government mobilizes different types of tools. Using Qwen-plus, a large language model developed by Alibaba Cloud and fine-tuned for OS-related content, each policy statement is categorized into one of fifteen subcategories under three main types: supply-oriented, environment-oriented, and demand-oriented tools. Our findings reveal a strong dominance of supply-oriented tools (63%), especially investments in infrastructure, education, and public services. Demand-oriented tools remain marginal (11%), with little use of economic incentives or regulatory obligations. Environment-oriented tools show more balance but still underrepresent key components like incentive systems and legal mandates for open access. To deepen the analysis, we introduce a normalized indicator of institutional focus, which captures the relative emphasis of each policy type across administrative levels. Results show supply-oriented tools are concentrated at top-level institutions, reflecting a top-down governance model. Demand tools are localized at lower levels, highlighting limited strategic commitment. Overall, China’s OS policy mix prioritizes infrastructure over incentives, limiting systemic transformation toward a more sustainable open science ecosystem.