IntroductionAcademic visibility is increasingly shaped by inclusion in major bibliometric databases, yet the extent to which these systems accurately represent scholarly contributions from small academic communities remains contested. This study examines how database selectivity, language hierarchies, and citation inequality influence the visibility of legal scholarship in a small-state context.MethodsUsing a comparative bibliometric analysis of Google Scholar, Scopus, and Web of Science, the study focuses on three leading law faculties in North Macedonia: Ss. Cyril and Methodius University in Skopje (UKIM), Goce Delcev University (UGD), and South East European University (SEEU).ResultsThe findings reveal a layered structure of academic visibility shaped by differences in database coverage and indexing practices. While Google Scholar captures a broad spectrum of scholarly output, Scopus and Web of Science provide increasingly selective representations, resulting in substantially lower levels of measurable research activity. Across the analyzed institutions, citation impact is unevenly distributed, with a small number of researchers accounting for a disproportionate share of total citations, although the precision of inequality estimates varies across institution-database combinations. Publication language emerges as an important factor associated with differences in citation visibility. English-language publications account for the dominant share of citations across the analyzed databases, whereas Macedonian- and Albanian-language outputs show substantially lower citation visibility despite their presence in overall publication volume.DiscussionThe findings suggest that international visibility is associated not only with research productivity, but also with linguistic and structural characteristics embedded within contemporary systems of scholarly communication. Academic visibility in small-state contexts is socially and institutionally constructed through the interaction of database inclusion, language dominance, and cumulative citation dynamics. The study contributes to debates on research evaluation and academic publishing by demonstrating how bibliometric systems may produce partial representations of scholarly activity, particularly in disciplines characterized by strong local and national orientations. The findings highlight the need for more context-sensitive approaches to assessing research impact in peripheral academic systems.
ObjectiveTo characterize HIIT research while testing how database membership, document eligibility, and keyword metadata affect bibliometric representations of the field.MethodsArchived Web of Science (WoS), Scopus, and PubMed exports were reconstructed through documented deduplication and retrospective computational metadata screening. The primary corpus comprised 7,141 research articles published from 1977 to the March 2026 snapshot. Analyses included database sensitivity, Bradford zones and cumulative-model fit, identifier-based Lotka estimation, source-specific citations, WoS reference co-citation, author-keyword mapping, volume-balanced thematic periods, and Scopus affiliation-country collaboration.ResultsOf 18,578 record instances, 8,365 unique records remained after deduplication; 1,224 were excluded under the documented rules. Annual output increased from 80 articles in 2013 to 898 in 2025. Scopus membership covered 94.17% of the primary union, compared with 71.63% for WoS and 71.92% for PubMed. Top-20 keyword sets were similar across single-source subsets (Jaccard = 0.905–1.000), whereas keyword-field and network-size choices produced 3–8 themes. The 19 core journal identities contributed 33.61% of articles. Among 24,074 Scopus author identifiers, the estimated Lotka exponent was β = 2.447; both the fitted and classical full-range models failed bootstrap goodness-of-fit tests (p = 0.001). WoS co-citation identified four strongly interconnected reference communities, and the primary author-keyword map contained seven themes. Including reviews increased the corpus to 8,074 and the PubMed-only contribution from 166 to 876 records.ConclusionBroad topic visibility was comparatively stable, but database-specific contributions and thematic granularity depended on eligibility and metadata decisions. Source-resolved indicators and explicit sensitivity analyses provide a more defensible representation than an unqualified merged-data map. Findings describe bibliographic structure, not intervention effectiveness; historical search-date precision and independent screening agreement remain limitations.
Reproducibility is a major challenge for responsible machine learning (ML) research, especially with low-code/no-code automated ML (LCNC AutoML) tools making ML methods accessible to researchers outside data science. Existing approaches to support research reproducibility are difficult to apply to LCNC AutoML due to a lack of shareable code, limited tool transparency, and varying levels of ML expertise among users. To inform the design of LCNC AutoML tools that support reproducible research, this design science study proposes a set of design principles utilizing the theoretical lens of Technology Affordances and Constraints Theory. We first identified design requirements based on user studies with six researchers who implemented and reproduced LCNC AutoML projects. This was followed by a technical review of eight LCNC AutoML tools to identify existing design features in relation to the identified design requirements. Through mapping design features to design requirements, a set of design principles emerged: (1) GUI Customizability, (2) Standardized Reporting, (3) Interoperability, and (4) Adaptive Guidance. Together, these principles provide actionable design guidance for developing LCNC AutoML tools that support the documentation and reproducibility of ML research.
IntroductionLarge language models (LLMs) are increasingly used for scoring and feedback in second-language (L2) assessment, yet the validity of the resulting interpretations remains contested. This review evaluated when LLM-mediated assessment is psychometrically and educationally defensible using an argument-based validity framework focused on construct representation, reliability and reproducibility, fairness, and washback.MethodsFollowing PRISMA 2020, we systematically searched nine databases and repositories for empirical studies published from January 2022 to December 2025. Fifty-two studies met the inclusion criteria. Quantitative synthesis used REML random-effects models where effects were sufficiently comparable; Pearson correlations, rank correlations, ICC/κ/QWK, and other psychometric indices were otherwise retained on metric-appropriate scales. Washback outcomes were synthesized using Hedges' g.ResultsSource-level auditing showed that directly comparable Pearson evidence was sparse. Three source-verified L2 studies yielded moderate human-LLM score correspondence (r = 0.66, 95% CI [0.53, 0.75]) with substantial heterogeneity (I2 = 70.8%). Evidence was strongest in rubric-guided, structurally constrained, and human-supervised contexts, whereas construct representation, fairness, operational reproducibility, and generalizability remained underdeveloped. Across 14 studies, LLM-mediated formative feedback produced a small-to-moderate positive effect on revision quality and short-term writing outcomes (g = 0.42, 95% CI [0.27, 0.57]), although heterogeneity was substantial (I2 = 69.8%) and concerns about cognitive offloading, learner agency, and reproducibility persisted.DiscussionThe findings support a context-dependent interpretation of validity rather than a universal claim about LLM assessment capability. Current evidence supports carefully bounded formative and human-supervised applications but does not justify autonomous high-stakes scoring or broad claims of construct validity, fairness, and generalizability.
This paper reflects on the current crisis of academic integrity caused by the phenomenon of fabricated and semantically incorrect citations. In the era of ‘fast science' and permanent citation inflation, human reviewers face extreme cognitive overload and time pressure, making it impossible to consistently verify literature sources. As a key solution, this paper proposes the deployment of autonomous AI agents (Agentic AI) designed for the deep semantic auditing of manuscripts. This technological shift is complemented by an innovative proposal to introduce mandatory authorial ‘citation reports' that operationalize the concepts of citation classification. The resulting synergy between human expertise and machine precision relieves reviewers of their mechanical burden and can help strengthen the rigor, transparency, and credibility of peer review.
