Large language model (LLM) chatbots, as a widely used form of generative artificial intelligence, have reduced the marginal cost of producing publication-style manuscripts and have expanded feasible routes for manipulating citation metrics within the publishing ecosystem. Citation-based indicators (e.g., the h-index, the i10-index, and total citation counts) remain embedded in research evaluation and are sensitive to indexing practices of bibliographic databases, with Google Scholar providing broad coverage combined with comparatively limited curation. In this study, a systematic literature review is conducted to synthesize reported mechanisms of citation-metric manipulation and to examine limitations of citation-metric use, including evidence reported in civil engineering. A Google Scholar proof-of-concept case study examines whether the indexing of LLM-assisted, non-peer-reviewed documents with concentrated references to a target author is associated with changes in author-level citation metrics under platform-specific conditions. After indexing, a stepwise increase in author-level metrics is observed, demonstrating the feasibility of citation-metric manipulation under the platform-specific conditions. Finally, this paper discusses the implications for research integrity and citation manipulation in the era of generative artificial intelligence. It also presents recommendations for researchers, academic institutions and evaluation committees, publishers and editors, bibliographic database providers, and funding institutions and policymakers.

Digital transformation of libraries is a key driver of the development of scholarly communication, open access, and knowledge management; however, its sustainability largely depends on institutional and legal conditions. This study examines the level of legal literacy among library professionals and the institutional barriers affecting the digital transformation of academic and public libraries in the Republic of Kazakhstan. A mixed-methods research design was employed, combining a quantitative online survey of library professionals with a qualitative analysis of open-ended responses provided by library managers. The findings indicate that, despite a relatively high level of basic digital skills, legal literacy related to copyright, licensing, and open access remains insufficiently institutionalized. Significant differences were identified between academic and public libraries, reflecting variations in their institutional missions and managerial priorities. Based on the interpretation of empirical results and their comparison with international literature, the study proposes a conceptual model of digital–legal transformation of libraries that integrates digital competencies, legal literacy, and institutional support mechanisms. Additionally, a phased roadmap is developed to support the sustainable implementation of open-access practices and the development of institutional repositories within a nascent open science ecosystem. The study’s results inform the development of institutional digital strategies for libraries and contribute to the design of professional development programs for library staff.

University rankings based on scientometric indicators frequently rely on compensatory aggregation models that allow extreme values to dominate the evaluation, while also remaining sensitive to outliers and unstable weighting procedures. These issues reduce the reliability and interpretability of the resulting rankings. This study proposes a hybrid BWM–GRA–PROMETHEE (BGP) framework that combines judgement-based weighting Best-Worst Method (BWM), outlier-resistant normalization Grey Relational Analysis (GRA), and a non-compensatory outranking method Preference Ranking Organization Methods for Enrichment Evaluation (PROMETHEE II). The framework is applied to an expert-validated set of scientometric indicators to generate more stable and behaviorally grounded rankings. The results show that the proposed method maintains stability under weight and threshold variations and preserves ranking consistency even under outlier-contaminated scenarios. Comparative experiments further demonstrate that BGP is more robust than Additive Ratio Assesment (ARAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), achieving the highest Spearman. This study contributes a unified evaluation framework that jointly addresses three major methodological challenges in scientometric ranking, outlier sensitivity, compensatory effects, and instability from data-dependent weighting. By resolving these issues within a single integrated model, the proposed BGP approach offers a more reliable and methodologically rigorous foundation for researchers and policymakers seeking to evaluate and enhance research performance.

Ranking research groups plays a crucial role in various contexts, such as ensuring the fair allocation of research grants, assigning projects, and evaluating journal editorial boards. In this paper, we analyze the distribution of h-indexes within research groups and propose a single metric to quantify their overall performance, termed the α-index. This index integrates two complementary aspects: the homogeneity of members’ h-indexes, captured by the Gini coefficient (g), and the h-group, an extension of the individual h-index to groups. By combining both uniformity and collective research output, the α-index provides a consistent and equitable metric for comparative evaluation, essentially calculated as the average relative h-group multiplied by (1−g) and normalized by the maximum value of this quantity across all analyzed groups. We describe the full procedure for computing the index and its components and illustrate its application to computer science conferences, where program committees are compared through a resampling procedure that ensures fair comparisons across groups of different sizes. Additional results are presented for postgraduate programs, further demonstrating the method’s applicability. Correlation analyses are used to establish rankings; however, our primary goal is to recommend a fairer index that reduces deviations from those currently used by governmental agencies to evaluate conferences and graduate programs. The proposed approach offers a more nuanced assessment than simply averaging members’ h-indexes and can be applied broadly–for example, to university departments and research councils–contributing to a more equitable distribution of research funding, an issue of increasing importance.

Recent decades have seen a rise of anti-science rhetoric, fueled by scientific scandals and failures of peer review, and the rise of trainable generative AI spreading misinformation. We argue, moreover, that the continued erosion of scientific authority also arises from inherent features in science and academia, including a reliance on publication as a method for gaining professional credibility and success. Addressing this multifaceted challenge necessitates a concerted effort across several key areas: strengthening scientific messaging, combating misinformation, rebuilding trust in scientific authority, and fundamentally rethinking academic professional norms. Taking these steps will require widespread effort, but if we want to rebuild trust with the public, we must make significant and structural changes to the production and dissemination of science.

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.

Granny Josie’s Notebooks are salvaged notebooks written in 1946–1947 during a rural domestic science course for girls. This study aims to extract historically valuable information on the culinary heritage of post-war Poland and the housekeeping role attributed to women, with a specific focus on assessing the performance of Large Language Models (LLMs) in corpus analysis. The research began with an inquiry into the historical context of the notebooks. Three notebooks were digitised and stored in data repositories, then converted into editable vector text. The corpus was analysed with AI, and the results were compared with a text profile prepared by qualified linguists. According to the AI, the texts’ characteristic features are descriptions of nearly ritual food preparation, serving, and table setting. Women appear as central figures in post-war Polish housekeeping, acting as guardians of the hearth and planners and preparers of meals. However, AI’s interpretations were often overly idealised, with embellished descriptions that did not fully reflect the actual text. The digitisation and analysis of Granny Josie’s Notebooks provided new information about the culinary heritage of post-war Poland and preserved these materials from oblivion, while offering insights into the potential application of LLMs in corpus analysis.

This study examines the evolution of Kazakhstani research in the fields of economics and business research. We analyzed Scopus and Dimensions records for 2019–2023 following a PRISMA-like workflow with fully reproducible queries and time-stamped data extractions. We implemented both Bradford’s law of scattering and collaboration analysis. We report both journal-level (Scopus) and publication-level (Dimensions) results as complementary perspectives. Results show the applicability of Bradford’s law of scattering to research publications in Economics, Econometrics, and Finance, Business, Management, and Accounting, and Decision Sciences in Kazakhstan and globally. Collaboration analysis highlights strong regional ties and diversification. Authorship analysis reveals that 85.1% of publications have a Kazakhstan-affiliated first author. Publications were classified into three categories: Kazakhstan-Only (59.0%, N = 1585), International Collaboration with Kazakhstan-led authorship (31.5%, N = 845), and International Collaboration with Non-Kazakhstan-led authorship (9.5%, N = 255). International collaborations had 69–92% higher citation impact than domestic-only publications. The expansion of citing countries doubled in 2023 compared with 2019. Our contributions to bibliometric analysis and science policy are two-fold. First, we provide a comprehensive comparative analysis of publication patterns between Kazakhstani and global journals in economics and business-related fields, revealing specific areas requiring development. Second, we identify collaboration patterns, including citation analysis of studied fields. The analysis revealed strategies that can be applied in other emerging economies.

The Chengdu–Chongqing Economic Circle (CCEC) represents a key regional development initiative in China. University research collaboration plays a vital role in advancing its innovation ecosystem and supporting sustainable growth. This study examines inter-university collaboration among 47 public universities in the CCEC based on 53,968 co-authored publications from 2005 to 2024. Using bibliometric and visualization techniques in CiteSpace 6.4.R2, it explores the structure and evolution of collaboration from institutional, thematic, and author perspectives. The results reveal a steady expansion of collaborative activities driven by national innovation strategies. Leading institutions such as Sichuan University, UESTC, and Chongqing University act as central hubs connecting diverse research communities. Collaboration has diversified from traditional fields toward interdisciplinary areas including materials, environmental science, and applied mathematics. Author networks are becoming more cohesive, reflecting stronger knowledge integration across universities. The study highlights how policy-driven collaboration fosters regional innovation capacity and provides evidence-based insights for strengthening university networks and advancing the CCEC’s role as a science and technology innovation hub in western China.

This article presents a scoping review of the academic literature published between 2021 and 2024 on the intersection of generative artificial intelligence (AI) and disinformation. Drawing from 64 peer-reviewed studies, the review examines the current research landscape and identifies six key thematic areas: political disinformation and propaganda; scientific disinformation; fact-checking; journalism and the media; media literacy and education; and deepfakes. The findings reveal that generative AI plays a dual role: it enables the rapid creation and targeted dissemination of synthetic content but also offers new opportunities for detection, verification, and public education. Beyond summarizing research trends, this review highlights the broader societal and practical implications of generative AI in the context of information disorder. It outlines how AI tools are already reshaping journalism, challenging scientific communication, and transforming strategies for media literacy and fact-checking. The analysis also identifies key policy and governance challenges, particularly the need for coordinated responses from governments, platforms, educators, and civil society actors. By offering a structured overview of the field, the article enhances our understanding of how generative AI can both exacerbate and help mitigate disinformation, and proposes directions for research, regulation, and public engagement.