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Publications, Volume 13, Issue 1 (March 2025) – 13 articles

Cover Story (view full-size image): The public release of ChatGPT in late 2022 sparked widespread discussion on the capabilities of generative AI language models. This paper evaluates the archaeological literature included in the training of ChatGPT4o, ScholarGPT, and DeepSeek R1, building on prior analysis of ChatGPT3.5. While ChatGPT3.5 provided seemingly relevant references, many were fictitious. ScholarGPT, designed for academic use, performed better but still produced a high rate of fake references compared to ChatGPT4o and DeepSeek. Using cloze analysis, the study found no evidence that these models accessed full texts of genuine references. Genuine references matched those cited on Wikipedia, suggesting reliance on third-hand sources, raising concerns about data quality and its implications for AI-generated responses. View this paper
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20 pages, 251 KiB  
Article
Diamond Open Access Landscape in Croatia: DIAMAS Survey Results
by Jadranka Stojanovski and Danijel Mofardin
Publications 2025, 13(1), 13; https://doi.org/10.3390/publications13010013 - 13 Mar 2025
Viewed by 923
Abstract
As open science initiatives address the crisis in scholarly communication driven by commercialisation, diamond open access publishing—promoting equity for authors and readers—has emerged as a focal point in open access scholarly publishing. This study examines the landscape of institutional publishing in Croatia, focusing [...] Read more.
As open science initiatives address the crisis in scholarly communication driven by commercialisation, diamond open access publishing—promoting equity for authors and readers—has emerged as a focal point in open access scholarly publishing. This study examines the landscape of institutional publishing in Croatia, focusing on the community-owned diamond open access model. Through the DIAMAS project survey, which targeted 251 institutional publishers and achieved a response rate of 77, the research identifies the distinct features of Croatian institutional publishing. Institutional publishers are characterised by governance structures, funding challenges, voluntary staffing, and alignment with open science principles. Notable traits include reliance on public funding, use of the national open access journal platform, and a strong diamond open access publishing tradition. Key findings emphasise the critical role of national infrastructure, services, and multilingual publishing. Persistent challenges include meeting indexing criteria, advancing open science practices, and ensuring metadata quality. This study provides a comprehensive mapping of Croatian institutional publishers, offering insights into their strengths and weaknesses while proposing strategies for improvement. The findings underscore the importance of national policy frameworks, capacity building, and international collaboration to ensure the sustainability and visibility of Croatian institutional publishing. Full article
23 pages, 665 KiB  
Article
The Origins and Veracity of References ‘Cited’ by Generative Artificial Intelligence Applications: Implications for the Quality of Responses
by Dirk H. R. Spennemann
Publications 2025, 13(1), 12; https://doi.org/10.3390/publications13010012 - 12 Mar 2025
Cited by 1 | Viewed by 2047
Abstract
The public release of ChatGPT in late 2022 has resulted in considerable publicity and has led to widespread discussion of the usefulness and capabilities of generative Artificial intelligence (Ai) language models. Its ability to extract and summarise data from textual sources and present [...] Read more.
The public release of ChatGPT in late 2022 has resulted in considerable publicity and has led to widespread discussion of the usefulness and capabilities of generative Artificial intelligence (Ai) language models. Its ability to extract and summarise data from textual sources and present them as human-like contextual responses makes it an eminently suitable tool to answer questions users might ask. Expanding on a previous analysis of the capabilities of ChatGPT3.5, this paper tested what archaeological literature appears to have been included in the training phase of three recent generative Ai language models: ChatGPT4o, ScholarGPT, and DeepSeek R1. While ChatGPT3.5 offered seemingly pertinent references, a large percentage proved to be fictitious. While the more recent model ScholarGPT, which is purportedly tailored towards academic needs, performed much better, it still offered a high rate of fictitious references compared to the general models ChatGPT4o and DeepSeek. Using ‘cloze’ analysis to make inferences on the sources ‘memorized’ by a generative Ai model, this paper was unable to prove that any of the four genAi models had perused the full texts of the genuine references. It can be shown that all references provided by ChatGPT and other OpenAi models, as well as DeepSeek, that were found to be genuine, have also been cited on Wikipedia pages. This strongly indicates that the source base for at least some, if not most, of the data is found in those pages and thus represents, at best, third-hand source material. This has significant implications in relation to the quality of the data available to generative Ai models to shape their answers. The implications of this are discussed. Full article
(This article belongs to the Special Issue AI in Open Access)
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24 pages, 9722 KiB  
Article
Automation Applied to the Collection and Generation of Scientific Literature
by Nadia Paola Valadez-de la Paz, Jose Antonio Vazquez-Lopez, Aidee Hernandez-Lopez, Jaime Francisco Aviles-Viñas, Jose Luis Navarro-Gonzalez, Alfredo Valentin Reyes-Acosta and Ismael Lopez-Juarez
Publications 2025, 13(1), 11; https://doi.org/10.3390/publications13010011 - 6 Mar 2025
Viewed by 879
Abstract
Preliminary activities of searching and selecting relevant articles are crucial in scientific research to determine the state of the art (SOTA) and enhance overall outcomes. While there are automatic tools for keyword extraction, these algorithms are often computationally expensive, storage-intensive, and reliant on [...] Read more.
Preliminary activities of searching and selecting relevant articles are crucial in scientific research to determine the state of the art (SOTA) and enhance overall outcomes. While there are automatic tools for keyword extraction, these algorithms are often computationally expensive, storage-intensive, and reliant on institutional subscriptions for metadata retrieval. Most importantly, they still require manual selection of literature. This paper introduces a framework that automates keyword searching in article abstracts to help select relevant literature for the SOTA by identifying key terms matching that we, hereafter, call source words. A case study in the food and beverage industry is provided to demonstrate the algorithm’s application. In the study, five relevant knowledge areas were defined to guide literature selection. The database from scientific repositories was categorized using six classification rules based on impact factor (IF), Open Access (OA) status, and JCR journal ranking. This classification revealed the knowledge area with the highest presence and highlighted the effectiveness of the selection rules in identifying articles for the SOTA. The approach included a panel of experts who confirmed the algorithm’s effectiveness in identifying source words in high-quality articles. The algorithm’s performance was evaluated using the F1 Score, which reached 0.83 after filtering out non-relevant articles. This result validates the algorithm’s ability to extract significant source words and demonstrates its usefulness in building the SOTA by focusing on the most scientifically impactful articles. Full article
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29 pages, 3309 KiB  
Article
Scientific Collaboration and Sustainable Development: A Bibliometric Analysis of the Andean Region, Panama, and Spain
by Gresky Gutiérrez-Sánchez, Patricio Álvarez-Muñoz, Purificación Galindo-Villardón and Purificación Vicente-Galindo
Publications 2025, 13(1), 10; https://doi.org/10.3390/publications13010010 - 27 Feb 2025
Cited by 1 | Viewed by 780
Abstract
Background: Scientific collaboration has become a cornerstone of sustainable development, particularly in regions where research capacity and funding face significant challenges. The Andean region, Panama, and Spain offer a unique perspective due to their cultural and linguistic ties, alongside varying levels of scientific [...] Read more.
Background: Scientific collaboration has become a cornerstone of sustainable development, particularly in regions where research capacity and funding face significant challenges. The Andean region, Panama, and Spain offer a unique perspective due to their cultural and linguistic ties, alongside varying levels of scientific production and innovation. These disparities present opportunities for collaboration and targeted interventions to foster regional growth and contribute to global priorities. According to UNESCO, Latin America invests merely 0.56% of its GDP in research and development, underscoring the pressing need for innovative strategies to enhance scientific capacity and align efforts with the United Nations Sustainable Development Goals (SDGs). Methods: This study employed HJ-Biplot and MANOVA-Biplot methodologies to analyze bibliometric data across various thematic areas. These multivariate techniques offer a comprehensive exploration of the interrelationships between scientific production, research talent, and international collaboration, revealing significant patterns and associations. The data were sourced from the Scimago Iberoamerican platform, which aggregates information from Elsevier’s Scopus database on scientific journals and countries. The platform provides data in five-year increments, capturing trends in scientific output, international collaboration, and thematic focus across the Andean region, Panama, and Spain, spanning the period from 2012 to 2022. Results: The analysis identified significant correlations between scientific productivity, research talent, and international partnerships. Clustering disciplines such as engineering, computer science, and energy highlights the strong intersections between technology and economic development. The proximity of psychology and environmental sciences emphasizes the importance of social and environmental factors in scientific research. Conclusion: This study provides a comprehensive bibliometric analysis of the Andean region, Panama, and Spain, identifying critical drivers of scientific productivity and collaboration. The integration of advanced statistical methodologies reveals key associations between research talent, international partnerships, and thematic focus areas. While areas such as environmental sciences and biochemistry demonstrate alignment with innovation and sustainability goals, disciplines like engineering and mathematics require targeted investment to enhance their contributions. These findings underscore the importance of a balanced approach to research funding and policymaking to ensure equitable and impactful scientific development across regions. The results serve as a roadmap for fostering collaboration, strengthening leadership, and aligning research efforts with sustainable development objectives globally. Full article
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17 pages, 449 KiB  
Article
Polarization in BRICS and G7: Scopus-Indexed Journal Production Trends (2013–2023)
by Eungi Kim, Sureshkrishnan Ramakrishnan and Jason Lim Chiu
Publications 2025, 13(1), 9; https://doi.org/10.3390/publications13010009 - 13 Feb 2025
Viewed by 1131
Abstract
The objective of this study is to examine disparities in Scopus-indexed journal production between BRICS and G7 countries from 2013 to 2023, focusing on growth trends, open access (OA) and non-OA production, subject representation, and quality metrics. Using data from the SCImago Journal [...] Read more.
The objective of this study is to examine disparities in Scopus-indexed journal production between BRICS and G7 countries from 2013 to 2023, focusing on growth trends, open access (OA) and non-OA production, subject representation, and quality metrics. Using data from the SCImago Journal Rank portal, the analysis evaluated growth rates, quartile rankings, and publisher dynamics. G7 countries maintained their global leadership, characterized by stable production systems and high-impact journals predominantly managed by commercial publishers. In contrast, the countries of Brazil, Russia, India, China, and South Africa (BRICS) exhibited diverse trends: China and Russia demonstrated rapid expansion through state-backed initiatives and the rise of domestic publishers, aiming to reduce reliance on foreign publishers and enhance global visibility. However, India experienced a decline, while Brazil and South Africa showed only modest growth in Scopus-indexed journal production. Similarly, G7 countries displayed internal variability, with the UK and Italy achieving notable growth, whereas Japan and France faced declines. These disparities within both groups underscore the critical influence of national research policies and infrastructure on journal production. BRICS countries showed a strong focus on STEM disciplines, with China emerging as a leader in both OA and non-OA journal production. Conversely, G7 countries maintained a balanced representation across STEM and social sciences. These findings suggest that national policies and infrastructure investments are key drivers of journal production growth, with BRICS countries leveraging new initiatives for expansion and G7 countries maintaining dominance through established systems. Full article
(This article belongs to the Special Issue Bias in Indexing: Effects on Visibility and Equity)
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27 pages, 5423 KiB  
Review
Mapping the Conceptual Structure of University–Industry Knowledge Transfer: A Co-Word Analysis
by Vladimir Alfonso Ballesteros-Ballesteros and Rodrigo Arturo Zárate-Torres
Publications 2025, 13(1), 8; https://doi.org/10.3390/publications13010008 - 12 Feb 2025
Viewed by 1148
Abstract
University–industry (U–I) collaborations are widely recognized as key drivers of economic progress, innovation, and competitiveness, fostering significant scholarly interest. Concurrently, research findings on these interactions have contributed to the establishment of an interdisciplinary field marked by the inherent complexity of these relationships. This [...] Read more.
University–industry (U–I) collaborations are widely recognized as key drivers of economic progress, innovation, and competitiveness, fostering significant scholarly interest. Concurrently, research findings on these interactions have contributed to the establishment of an interdisciplinary field marked by the inherent complexity of these relationships. This study aims to map the conceptual structure of university–industry knowledge transfer (UIKT) research from 1980 to 2023 by employing co-word analysis and social network analysis based on data retrieved from the Scopus database. The results reveal that 1577 documents were published during this period, incorporating 147 keywords, with the five most frequent being “innovation”, “higher education”, “university”, “technology transfer”, and “knowledge management”. The United Kingdom was identified as the most prolific country, contributing 366 documents, while Research Policy emerged as the most cited journal, with 3546 citations. This study offers a comprehensive overview of the current state of UIKT research, paving the way for future studies and providing valuable directions for further investigations. Full article
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11 pages, 2284 KiB  
Review
Meta-Research in Biomedical Investigation: Gaps and Opportunities Based on Meta-Research Publications and Global Indicators in Health, Science, and Human Development
by Ivan David Lozada-Martinez, David A. Hernandez-Paz, Ornella Fiorillo-Moreno, Yelson Alejandro Picón-Jaimes and Valmore Bermúdez
Publications 2025, 13(1), 7; https://doi.org/10.3390/publications13010007 - 10 Feb 2025
Viewed by 1017
Abstract
Meta-research in biomedical science is crucial for ensuring rigour, relevance, and transparency in an era marked by the exponential growth of scientific publications. This study examines global and historical trends in meta-research activities within biomedicine and investigates their relationship with health, science, and [...] Read more.
Meta-research in biomedical science is crucial for ensuring rigour, relevance, and transparency in an era marked by the exponential growth of scientific publications. This study examines global and historical trends in meta-research activities within biomedicine and investigates their relationship with health, science, and human development indicators. A systematic analysis of 9633 publications from Scopus, Web of Science, and PubMed was conducted, focusing on publication volume, citation impact, and geographic distribution. Regression analyses reveal a significant positive association between meta-research activity and the Human Development Index (HDI), suggesting that meta-research contributes to societal advancement by enhancing evidence-based decision-making in health. However, no association was found between meta-research output and research and development (R&D) expenditure, reflecting the minimal resource requirements of secondary data-driven studies compared to primary or experimental research. Meta-research activity correlates positively with clinical trial completion, indicating its role in refining study designs and addressing evidence gaps. These findings highlight the importance of expanding meta-research in underrepresented regions to promote equity in scientific advancement and improve the reliability of biomedical knowledge. This result underscores the need for targeted support for meta-research, particularly in low- and middle-income countries with limited scientific infrastructure and resources for new knowledge generation. Full article
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17 pages, 1917 KiB  
Article
Forecasting the Scientific Production Volumes of G7 and BRICS Countries in a Comparative Analysis
by Tindaro Cicero
Publications 2025, 13(1), 6; https://doi.org/10.3390/publications13010006 - 7 Feb 2025
Viewed by 1538
Abstract
This study applies ARIMA models to forecast scientific production trends among G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and BRICS (Brazil, Russia, India, China, and South Africa) countries using Scopus data from 1996 to 2023. The analysis [...] Read more.
This study applies ARIMA models to forecast scientific production trends among G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and BRICS (Brazil, Russia, India, China, and South Africa) countries using Scopus data from 1996 to 2023. The analysis shows that G7 countries maintain steady growth driven by established research infrastructures, while BRICS nations, particularly China, display accelerated growth due to substantial investments in R&D. The forecasts indicate that China could reach over 2,000,000 indexed scientific publications annually by 2030, potentially reshaping the global research landscape. These findings provide valuable insights for policymakers and research institutions, highlighting the shifting dynamics of global scientific leadership and emphasizing the importance of sustained investment in research to remain competitive. Full article
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20 pages, 506 KiB  
Article
Social Media Analysis of High-Impact Information and Communication Journals: Adoption, Use, and Content Curation
by Jesús Cascón-Katchadourian, Javier Guallar and Wileidys Artigas
Publications 2025, 13(1), 5; https://doi.org/10.3390/publications13010005 - 17 Jan 2025
Viewed by 2703
Abstract
The use of social media to disseminate academic content is increasing, particularly in scientific journals. This study has the following two main objectives: first, exploring the use of social media by high-impact academic journals in two different SJR categories (Library and Information Sciences [...] Read more.
The use of social media to disseminate academic content is increasing, particularly in scientific journals. This study has the following two main objectives: first, exploring the use of social media by high-impact academic journals in two different SJR categories (Library and Information Sciences and Communication), and second, analyzing content curation carried out by the world’s most influential journals in both areas. The research methodology is descriptive with a quantitative approach regarding the items studied. The study finds that COM journals have a stronger social media presence than LIS journals, and X dominates in both categories and regions as the top social network, with significant influence as the only platform. On the other hand, content curation was found to a high degree in both areas, especially in the LIS area, with 93% vs. 80% in COM. The study highlights that both COM and LIS journals primarily focus on promoting recent articles, with COM diversifying content more than LIS. In terms of the content curation techniques used in both areas, the majority are abstracting and summarizing. Full article
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9 pages, 606 KiB  
Article
Analyzing the Drivers Behind Retractions in Tuberculosis Research
by Franko O. Garcia-Solorzano, Shirley M. De la Cruz Anticona, Mario Pezua-Espinoza, Fernando A. Chuquispuma Jesus, Karen D. Sanabria-Pinilla, Christopher Chavez Veliz, Vladimir A. Huayta-Alarcón, Percy Mayta-Tristan and Leonid Lecca
Publications 2025, 13(1), 4; https://doi.org/10.3390/publications13010004 - 14 Jan 2025
Viewed by 1085
Abstract
Tuberculosis research plays a crucial role in understanding and responding to the necessities of people with this disease, yet the integrity of this research is compromised by frequent retractions. Identifying and analyzing the main reasons for retraction of tuberculosis articles is essential for [...] Read more.
Tuberculosis research plays a crucial role in understanding and responding to the necessities of people with this disease, yet the integrity of this research is compromised by frequent retractions. Identifying and analyzing the main reasons for retraction of tuberculosis articles is essential for improving research practices and ensuring reliable scientific output. In this study, we conducted an advanced systematic literature review of retracted original articles on Tuberculosis, utilizing databases such as Web of Science, Embase, Scopus, PubMed, LILACS, and the Retraction Watch Database webpage. We found that falsification and plagiarism were the most frequent reasons for retraction, although 16% of the retracted articles did not declare the drivers behind the retraction. Almost half of the retracted studies received external funding, affecting not only those specific studies but future funding opportunities for this research field. Stronger measures of research integrity are needed to prevent misconduct in this vulnerable population. Full article
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8 pages, 591 KiB  
Opinion
Output-Normalized Score (OnS) for Ranking Researchers Based on Number of Publications, Citations, Coauthors, and Author Position
by Antonije Onjia
Publications 2025, 13(1), 3; https://doi.org/10.3390/publications13010003 - 4 Jan 2025
Viewed by 1122
Abstract
This article discusses current methods for ranking researchers and proposes a new metric, the output-normalized score (OnS), which considers the number of publications, citations, coauthors, and the author’s position within each publication. The proposed OnS offers a balanced approach to evaluating a researcher’s [...] Read more.
This article discusses current methods for ranking researchers and proposes a new metric, the output-normalized score (OnS), which considers the number of publications, citations, coauthors, and the author’s position within each publication. The proposed OnS offers a balanced approach to evaluating a researcher’s scientific contributions while addressing the limitations of widely used metrics such as the h-index and its modifications. It favors publications with fewer coauthors while giving significant weight to both the author’s position in the publication and the total number of citations. Full article
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12 pages, 888 KiB  
Article
Practicing Meta-Analytics with Rectification
by Ramalingam Shanmugam and Karan P. Singh
Publications 2025, 13(1), 2; https://doi.org/10.3390/publications13010002 - 2 Jan 2025
Cited by 1 | Viewed by 876
Abstract
This article demonstrates the necessity of assessing homogeneity in meta-analyses using the Higgins method. The researchers realize the importance of assessing homogeneity in meta-analytic work. However, a significant issue with the Higgins method has been identified. In this article, we explain the nature [...] Read more.
This article demonstrates the necessity of assessing homogeneity in meta-analyses using the Higgins method. The researchers realize the importance of assessing homogeneity in meta-analytic work. However, a significant issue with the Higgins method has been identified. In this article, we explain the nature of this problem and propose solutions to address it. Our narrative in this article is to point out the problem, analyze it, and present it well. A prerequisite to check the consistency of findings in comparable studies in meta-analyses is that the studies should be homogeneous, not heterogeneous. The Higgins I2 score, a version of the Cochran Q value, is commonly used to assess heterogeneity. The Higgins score is an improvement in the Q value. However, there is a problem with Higgins score statistically. The Higgins score is supposed to follow a Chi-squared distribution, but it does not do so because the Chi-squared distribution becomes invalid once the Q score is less than the degrees of freedom. This problem was recently rectified using an alternative method (S2 score). Using this method, we examined 14 published articles representing 133 datasets and observed that many studies declared homogeneous by the Higgins method were, in fact, heterogeneous. This article urges the research community to be cautious in making inferences using the Higgins method. Full article
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11 pages, 235 KiB  
Opinion
Exploring the Need to Use “Plagiarism” Detection Software Rationally
by Petar Milovanovic, Tatjana Pekmezovic and Marija Djuric
Publications 2025, 13(1), 1; https://doi.org/10.3390/publications13010001 - 2 Jan 2025
Viewed by 1673
Abstract
Universities and journals increasingly rely on software tools for detecting textual overlap of a scientific text with the previously published literature to detect potential plagiarism. Although software outputs need to be carefully reviewed by competent humans to verify the existence of plagiarism, university [...] Read more.
Universities and journals increasingly rely on software tools for detecting textual overlap of a scientific text with the previously published literature to detect potential plagiarism. Although software outputs need to be carefully reviewed by competent humans to verify the existence of plagiarism, university and journal staff, for various reasons, often erroneously interpret the degree of plagiarism based on the percentage of textual overlap shown in the similarity report. This is often accompanied by explicit recommendations to the author(s) to paraphrase the text to achieve an “acceptable” percentage of overlap. Here, based on the available literature and real-world examples from similarity reports, we provide a classification with extensive examples of phrases that falsely inflate the similarity index and argue the futility and dangers of rephrasing such statements just for the sake of reducing the similarity index. The examples provided in this paper call for a more reasonable assessment of text similarity. To fully endorse the principles of academic integrity and prevent loss of clarity of the scientific literature, we believe it is important to shift from pure bureaucratic and quantificational view on the originality of scientific texts to human-centered qualitative assessment of the manuscripts, including the software outputs. Full article
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