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        <title>Frontiers in Research Metrics and Analytics | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/research-metrics-and-analytics</link>
        <description>RSS Feed for Frontiers in Research Metrics and Analytics | New and Recent Articles</description>
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        <pubDate>2026-05-24T17:55:54.701+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1752085</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1752085</link>
        <title><![CDATA[Research on the pathways to enhance industry-academia-research institute collaboration and innovation: from the perspective of subway opening]]></title>
        <pubdate>2026-05-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Qianchun Dai</author><author>Li Yuanheng</author><author>Chengsheng Tong</author>
        <description><![CDATA[As a crucial component of urban transportation infrastructure, the subway not only optimizes the urban transportation network but also accelerates the flow and aggregation of innovative resources, holding significant importance for enhancing the level of Industry-academia-research cooperation among enterprises. This study utilizes panel data from A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2022, employing a multi-time point difference-in-differences method to delve into the promotional effect of subway opening on enterprise Industry-academia-research cooperation. The findings reveal that the opening of a subway significantly elevates the level of Industry-academia-research cooperation among enterprises, and this conclusion remains valid after a series of robustness tests. Mechanism analysis indicates that subway opening primarily promotes enterprise Industry-academia-research cooperation through three pathways: enhancing corporate cooperation culture, increasing the proportion of high-quality talent in enterprises, and elevating the level of digital transformation. Heterogeneity analysis shows that the promotional effect of subway opening on the level of enterprise Industry-academia-research cooperation is more pronounced in non-state-owned enterprises, high-tech enterprises, and small-scale enterprises. The research conclusions provide reliable empirical support for the promotion of enterprise Industry-academia-research cooperation by subway opening and offer insights and references for the government to further advance decision-making related to transportation infrastructure construction and high-quality enterprise development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1740510</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1740510</link>
        <title><![CDATA[Use of artificial intelligence tools in the publishing process: expectations from publishers through author guidelines]]></title>
        <pubdate>2026-05-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Josiline Chigwada</author><author>Patrick Ngulube</author>
        <description><![CDATA[IntroductionThe rapid integration of artificial intelligence (AI) into scholarly publishing has prompted publishers to develop policies that guide its ethical and practical use. This study explored the question, “What do publishers expect on the use of AI as portrayed in the author guidelines and AI publisher policies?” The investigation was guided by internationally recognized frameworks, including the STM ethical and practical guidelines on AI use, the Committee on Publication Ethics, the World Association of Medical Editors (WAME) recommendations on chatbots and generative AI in scholarly publishing, and the International Committee of Medical Journal Editors (ICMJE) standards.MethodsA qualitative research approach was employed, drawing from AI-related guidelines and policies from major scholarly publishers. Four publishers were purposively selected based on their global reach, relevance across multiple disciplines, and established AI policies. Data collection was conducted through document and web content analysis, which systematically extracted relevant information from publishers' websites.ResultsA qualitative research approach was employed, drawing from AI-related guidelines and policies from major scholarly publishers. Four publishers were purposively selected based on their global reach, relevance across multiple disciplines, and established AI policies. Data collection was conducted through document and web content analysis, which systematically extracted relevant information from publishers' websites. The findings revealed that AI policies are not standardized across publishers; instead, each adopts its own unique rules regarding AI-assisted writing, authorship, peer review, and disclosure.DiscussionWhile prior studies have documented the prevalence of disclosure requirements and authorship prohibitions, the present study moves beyond inventory to critically examine how international frameworks are reflected, adapted, and contested within individual publisher policies, and to surface tensions around enforceability, equity, and disciplinary variability that remain underexplored in existing literature. Consequently, researchers are required to carefully consult the specific policies of their target publishers before submission. The study recommends that all stakeholders in the publishing process be informed about AI-related policies relevant to their roles. Authors should ensure that AI outputs are accurate, reviewers must remain vigilant about unauthorized AI use, and publishers should proactively communicate best practices to maintain integrity and transparency in the scholarly publishing process.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1796649</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1796649</link>
        <title><![CDATA[Early-career researchers in Chile: findings from a national survey on conditions, challenges, and institutional perceptions]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Alexia Nunez-Parra</author><author>Andrés E. Marcoleta</author><author>Cristóbal Feller</author><author>Rodrigo Herrera-Camus</author><author>Valentina Parra</author>
        <description><![CDATA[Chile's investment in postgraduate training has produced a highly qualified cohort of early-career researchers. Yet little is known about how these researchers experience employment conditions, access to competitive funding, and the institutional environment in which they pursue their careers. Here, we report findings from a national survey of 267 early-career researchers working in Chile, examining training trajectories, employment conditions, grant-seeking experiences, time allocation, caregiving responsibilities, and perceptions of recent institutional reforms. Respondents reported high levels of intrinsic job satisfaction, particularly regarding intellectual challenge, autonomy, and the social value of their work. At the same time, they expressed concerns about contractual instability, workload intensity, and insufficient income. Nearly half held a second job, and those with caregiving responsibilities allocated less time to research. Care responsibilities were more frequently reported by women, who devoted substantially more time to caregiving than men. Participation in competitive funding programs varied across career stages, with higher application and success rates in entry-level schemes than in more advanced grant competitions. Gender differences were also observed in progression toward senior funding opportunities. Notably, a substantial proportion of unfunded proposals had received positive evaluations, suggesting that part of the country's research capacity remains unsupported despite being competitively viable. This reveals an unrealized scientific potential and a gap between Chile's expanding scientific base and the public investment available to sustain it. Although recent institutional reforms were perceived as contributing to greater diversity and regional participation, respondents identified limited improvements in overall funding levels and the international impact of Chilean research. These findings provide an empirical characterization of the working conditions and career challenges faced by early-career researchers in Chile, highlighting persistent structural barriers related to career consolidation, funding access, gender equity, and work-life balance.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1815503</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1815503</link>
        <title><![CDATA[How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017–2024)]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Andy Wai Kan Yeung</author><author>Deepal Haresh Ajmera</author><author>Andrew Nalley</author><author>Ray Tanaka</author><author>Kuo Feng Hung</author><author>Michael M. Bornstein</author>
        <description><![CDATA[ObjectivesTo quantify how artificial intelligence (AI) publications contribute to journal impact factor (JIF) in oral and maxillofacial radiology (OMFR) journals and to discuss implications for imaging research, peer review, and clinical translation.MethodsOn 25 June 2025, Journal Citation Reports (JCR) data (2017–2024) were retrieved for Dentomaxillofacial Radiology, Oral Radiology, Imaging Science in Dentistry, and the Radiology section of Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology (OOOO). For each JCR year, citable items and JIF-accountable citations were exported and manually coded as AI article, AI review, non-AI article, or non-AI review by two observers (κ = 0.956). A descriptive indicator, named as notional JIF, was computed to illustrate the contribution of AI items. Citation rates were compared descriptively across document types.ResultsIn JCR 2024, AI papers represented 10.9%−25.2% of citable items among OMFR journals yet contributed 31.3%−53.7% of JIF-accountable citations; radiology AI papers contributed materially to OOOO's JIF despite comprising only 1.5%−2.4% of citable items. AI articles and reviews received 4.3–4.4 × more JIF-accountable citations per item than non-AI counterparts. Notional JIFs exceeded actual JIFs in 2020–2022, reflecting small-denominator effects and topic-specific citation acceleration that diminished as more AI papers were published in subsequent years.ConclusionsAI-related publications are associated with higher per-item citation rates in OMFR journals, consistent with recognized patterns of topic-focused citation concentration in fast-moving research areas. These descriptive findings highlight the importance of methodological transparency, robust validation practices, and balanced editorial policies as AI research continues to expand.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1783120</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1783120</link>
        <title><![CDATA[On the pursuit of the true impact of our actions in science]]></title>
        <pubdate>2026-05-05T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Massyel S. Martínez-Cortés</author><author>José L. Medina-Franco</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1807672</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1807672</link>
        <title><![CDATA[Evaluating large language models for abstract evaluation tasks: an empirical study]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yinuo Liu</author><author>Emre Sezgin</author><author>Eric A. Youngstrom</author>
        <description><![CDATA[IntroductionLarge language models (LLMs) show great promise as tools for assisting scientific peer review, but their agreement with human experts in quantitative assessment of academic content needs further investigation. This study examined ChatGPT-5, Gemini-3-Pro, and Claude-Sonnet-4.5′s consistency and reliability in evaluating conference abstracts compared to one another and to human reviewers.MethodsThree LLMs independently graded 160 abstracts from a regional conference, while 14 human reviewers each assessed a subset using an identical rubric with eight criteria scored on a 1–5 scale. We compared AI and human scoring patterns using boxplots, calculated intraclass correlation coefficients (ICCs) for inter-rater reliability both among LLMs and between human and LLMs, and examined Bland-Altman plots to identify agreement patterns and systematic bias.ResultsThree LLMs demonstrated high internal consistency with narrow interquartile ranges and few outliers in composite scores, while human reviewers exhibited greater scoring variability. LLMs also achieved good-to-excellent agreement with each other across all criteria (ICCs: 0.59–0.87). ChatGPT and Claude reached moderate agreement with human reviewers on overall quality and content-specific criteria, with ICCs = 0.45–0.60 for composite score, impression, clarity, objective, and results. The two LLMs' concordance with humans achieved fair levels on subjective dimensions, with ICC ranging from 0.23–0.38 for impact, engagement, and applicability. Gemini performed notably worse, showing fair agreement on half the criteria and poor reliability on impact and applicability. Bland-Altman analysis revealed acceptable or negligible systematic bias, with mean differences of 0.24 (ChatGPT), 0.42 (Gemini), and −0.02 (Claude) from human mean ratings.DiscussionWith appropriate model selection, LLMs could reach moderate agreement with human experts on abstract overall quality and objective criteria, supporting their potential use for pre-screening low-quality submissions or serving as additional reviewers. Their ability to apply rubrics consistently across large volumes of abstracts offers advantages in efficiency and standardization that exceed human feasibility. However, LLMs' reduced performance on subjective dimensions indicates that they should complement rather than replace human judgment in abstract evaluation, with expert review remaining essential for comprehensive assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1793664</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1793664</link>
        <title><![CDATA[Beyond cumulative impact: the he-index and a volume-normalized efficiency metric for research evaluation]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohamd Laimon</author>
        <description><![CDATA[Traditional bibliometric indicators such as the h-index emphasize cumulative citation impact but provide limited insight into the efficiency and concentration of that impact across a researcher's publication portfolio. This study introduces the he-index as an interpretable indicator of the proportion of publications represented by the h-index core, relative to the researcher's total publication output. Using publicly accessible Scopus data from 54 researchers across engineering, medicine, and social sciences in the United States, China, and Australia, we examine the behavior of the he-index in relation to publication volume, career stage, and disciplinary context. As expected for a ratio-based indicator, the he-index exhibits a strong negative association with total publications (Spearman ρ = −0.77, p < 0.001), which may reflect structural sensitivity to publication volume. To improve cross-volume interpretability, we further propose a volume-normalized efficiency metric (he* = h/ĥ), where the expected h-index is estimated using a power-law scaling model (ĥ = 1.02N0.7, R2 ≈ 0.87). The normalized metric shows no significant dependence on publication volume (ρ = 0.064, p = 0.645) and exhibits weaker career-stage sensitivity, while discipline-level differences remain non-significant. External validation using field-weighted citation impact (FWCI) available for a subset of researchers provides additional support for the normalized metric, with he* positively associated with FWCI (ρ = 0.419, p = 0.003), whereas the raw he-index shows no significant association. Overall, the findings indicate that impact concentration and volume-normalized citation efficiency offer complementary perspectives to cumulative impact metrics, supporting more nuanced and multidimensional research evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1786866</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1786866</link>
        <title><![CDATA[Research information systems and knowledge graphs: a review]]></title>
        <pubdate>2026-05-04T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Muhammad Haris</author><author>Sören Auer</author><author>Markus Stocker</author>
        <description><![CDATA[Digital research artifacts (articles, datasets, software, etc.) are the basis for scientific research. Due to the continuous growth and complexity (e.g., due to formats) of such artifacts, ensuring their organization and long-term availability for research is becoming increasingly challenging. It is essential to manage the artifacts so that they are easily discoverable, accessible and useable by relevant communities. Research Information Systems (RISs) have become indispensable in curating, managing, and publishing research artifacts and other research objects. Diverse communities actively use the data from these systems to conduct research-intensive activities across various fields, including computer science, engineering, and life sciences. We review the current state of the art in research information systems, specifically: scholarly identifier systems, bibliographic databases, Research Data Management (RDM) services, and Knowledge Graphs (KGs). These infrastructures play a crucial role in the management of research artifacts. First, we discuss infrastructures that enable the persistent identification of research artifacts to make them globally discoverable and citeable. Second, we discuss databases that manage metadata about research artifacts. Third, we present RDM services that support publishing and accessing research data. Finally, we provide a comprehensive overview of domain-specific and domain-agnostic KGs and databases that have been widely adopted to represent scientific knowledge in different domains in structured form.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1772126</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1772126</link>
        <title><![CDATA[Healthcare analytics perspectives on artificial intelligence–based retinopathy of prematurity screening: a bibliometric mapping study]]></title>
        <pubdate>2026-04-30T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Amina Turki</author><author>Nazar Salih Abdulhussein</author><author>Rasha W. Mohammed Taher</author><author>Mohamed Ksantini</author>
        <description><![CDATA[The integration of artificial intelligence (AI) into retinopathy of prematurity (ROP) screening represents an important advancement in neonatal ophthalmology, particularly in settings facing specialist shortages and screening infrastructure constraints. Despite the rapid expansion of research in this domain, a structured evaluation of its intellectual landscape, geographic distribution, and collaboration patterns remains limited. This study presents a bibliometric mapping analysis of global research on AI-based ROP screening. A total of 55 publications indexed in the Scopus database were identified through a reproducible search strategy and analyzed using Bibliometrix and VOSviewer. The retrieved publications span the period 2017–2025. The results demonstrate a marked upward trajectory in publications, with a peak of 16 publications in 2025 and an estimated annual growth rate of 41.42%. The analysis examines publication trends, geographic productivity, institutional collaboration networks, citation patterns, and thematic evolution. The findings indicate strong contributions from Asia and increasing participation from the Middle East, particularly Türkiye, Saudi Arabia, and Egypt. International co-authorship networks reveal structured South–North collaborations linking Middle Eastern countries with major research hubs in the United States and the United Kingdom. Citation analysis shows an average of 18.27 citations per publication. Thematic mapping highlights the dominance of deep learning and retinal image analysis, alongside emerging discussions on healthcare accessibility and deployment in resource-limited settings. These results provide a structured quantitative overview of the development of AI-based ROP screening research and may support future research prioritization and strategic planning in AI-assisted neonatal eye care.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1807122</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1807122</link>
        <title><![CDATA[When retraction replaces rebuttal: suppression of critical scholarship on parental alienation]]></title>
        <pubdate>2026-04-28T00:00:00Z</pubdate>
        <category>Opinion</category>
        <author>Keith Robert Head</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1843420</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1843420</link>
        <title><![CDATA[Correction: Application of the cyberinfrastructure production function model to R1 institutions]]></title>
        <pubdate>2026-04-27T00:00:00Z</pubdate>
        <category>Correction</category>
        <author>Preston M. Smith</author><author>Jill Gemmill</author><author>David Y. Hancock</author><author>Brian W. O'Shea</author><author>Winona Snapp-Childs</author><author>James Wilgenbusch</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1770226</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1770226</link>
        <title><![CDATA[Impact of leading plant research centers of excellence on the scientific and socio-economic development in Europe]]></title>
        <pubdate>2026-04-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Petar Kazakov</author><author>Dimo Atanasov</author><author>Rositsa Beluhova</author><author>Marina Mircheva-Topalova</author><author>Asya Ivanova</author><author>Vesela Kazashka</author><author>Tsanko Gechev</author>
        <description><![CDATA[BackgroundCenters of Excellence (CoEs) are key instruments for advancing scientific excellence, innovation, and socio-economic development in Europe.MethodsThe paper compares three leading European Centers of Excellence in plant science: the Max Planck Institute of Molecular Plant Physiology (Potsdam-Golm, Germany), the Center for Plant Systems Biology at the Flanders Institute of Biotechnology (Gent, Belgium), and the Center of Plant Systems Biology and Biotechnology (Plovdiv, Bulgaria). The survey explores their organizational structures, funding sources, scientific focus and impact on the regional socio-economic environment.ResultsAll centers impose substantial influence on the scientific advancement of their countries. The study indicates that the contributions of these institutions extend beyond national borders, supporting scientific progress at the European and global level.ConclusionIn addition to research impact and innovation potential, these institutions enable socio-economic development of their regions by improving local infrastructure, creating jobs, promoting a favorable business environment, and facilitating stronger linkages between academia and industry.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1779778</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1779778</link>
        <title><![CDATA[A review of beyond citations for books: integrating library holdings and altmetrics in the impact evaluation of scholarly books and textbooks]]></title>
        <pubdate>2026-04-14T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Ashraf Maleki</author>
        <description><![CDATA[As scholarly books remain vital outputs in the humanities, social sciences, and education, new approaches are required to capture their diverse impacts. This study explores the multidimensional evaluation of academic books and textbooks by integrating citation metrics, alternative indicators, and (dis)aggregated library holdings data. Drawing on three large-scale empirical datasets encompassing over 119,000 Scopus-indexed book titles across 26 disciplines, the interactions and divergences among library print holdings (LPH), library electronic holdings (LEH), total library holdings (TLH), and other non-traditional metrics were examined in relation to their capacity to reflect scholarly and educational impacts. Library holdings data are among the most widely available book metrics (covering 97% of titles); however, aggregating print and electronic holdings into a single TLH metric has been shown to obscure important differences. Print holdings, though declining in average count over time, exhibit more stable, cumulative characteristics that statistically align with formal citation patterns, whereas electronic holdings reveal uneven acquisitions and inflated volumes that reduce the predictive strength of TLH. Print holdings outperform electronic holdings in modeling scholarly and educational impacts across most fields, except platforms such as Mendeley and Goodreads, which are better aligned with electronic availability. In a related investigation of textbooks, educational relevance is assessed through Open Syllabus Project rankings and compared with citation-based indicators (Scopus citations and book-to-book citation-based PageRank and HITS rankings), Goodreads user metrics, and WorldCat edition counts. Disciplinary differences emerge as prominent predictors of uptake across metrics. Goodreads ratings provide the strongest predictions in the humanities, WorldCat editions in the social sciences and medicine, and authority scores in citation networks in law and political science, improving the predictability of educational influence. Assessment of scholarly books benefits from acknowledging format-in-use and multidimensional usage patterns. As books often operate across scholarly, educational, and social contexts, their assessments should reflect blended purposes and audiences. Although a book's stated audience and intent may suggest a dominant context, substantial variation exists across titles. This provides a conceptual synthesis to integrate findings from large-scale quantitative studies on library holdings, citations, and altmetrics (alternative metrics) to present a more robust approach for assessing the multidimensional impact of academic books.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1774316</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1774316</link>
        <title><![CDATA[Editorial: Feminist methodologies in research on violence, displacement, and power]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Alina Potts</author><author>Anny Modi</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1781697</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1781697</link>
        <title><![CDATA[Navigating the ethical landscape of scholarly publishing: a comparative evaluation of Gemini and DeepSeek LLMs in addressing authorship and contributorship disputes]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kannan Sridharan</author><author>Gowri Sivaramakrishnan</author>
        <description><![CDATA[BackgroundThe rising complexity of publication ethics, particularly authorship disputes, necessitates exploring Large Language Models (LLMs) as potential evaluative tools. This study compares the performance of Google Gemini 2.5 Flash and DeepSeek-V3.2 against expert Committee on Publication Ethics (COPE) forum responses.MethodsA cross-sectional analysis including 12 COPE authorship and contributorship cases was conducted using three prompting strategies: Minimal, Deterministic, and Stochastic. Responses were scored across seven domains on a 5-point Likert scale (1 = poor, 5 = excellent) by independent raters.ResultsBoth LLMs achieved perfect scores (5 ± 0) in Actionability of Recommendations and high marks in Safety and Avoidance of Hallucination (4.88 ± 0.33). In the Consistency with COPE Principles domain, DeepSeek performed slightly better than Gemini (4.45 ± 1.0 vs. 4.12 ± 1.29), while Gemini showed a better Overall Appropriateness (4.03 ± 0.98 vs. 3.82 ± 1.29) but they were not statistically significant. Both models struggled most with Identification of Ethical Issues (Gemini: 3.91 ± 1.33; DeepSeek: 3.82 ± 1.29). Under Minimal prompts, Gemini's ethical identification was lower (3.55 ± 1.44) compared to Deterministic/Stochastic prompts (4.09 ± 1.3). Qualitatively, Gemini recorded an 8% major disagreement rate with COPE, while DeepSeek had a 16% combined (minor and major) disagreement rate. Mean similarity scores to COPE forum experts were approximately 4 for both models. Both models missed specific legal/copyright nuances but provided unique “value-add” strategies, such as author disassociation statements and editorial de-escalation training, not present in original COPE forum advice.ConclusionLLMs demonstrated high degree of alignment with COPE expert ethical reasoning. While they possess a “legal blind spot,” their ability to provide actionable and clear guidance, optimized through structured prompting, makes them valuable supplementary tools for journal editors.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1766504</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1766504</link>
        <title><![CDATA[Scientific autonomy in the structural bubble: from institutional bias to AI-mediated consensus]]></title>
        <pubdate>2026-04-07T00:00:00Z</pubdate>
        <category>Hypothesis and Theory</category>
        <author>Elias Rubenstein</author>
        <description><![CDATA[Most professional science is produced inside institutions whose survival depends on competitive funding, political legitimacy, and reputation management. Under these conditions, knowledge production does not unfold in a neutral space of ideas but within a structurally constrained environment—a structural bubble—in which only some questions, methods, and conclusions are likely to be funded, published, and disseminated at scale. This article models four interacting layers: (1) institutional funding and conflicts of interest; (2) publication systems, peer review, and metrics as selection and valuation mechanisms; (3) consensus infrastructures (policy-governed encyclopedic and secondary synthesis platforms) that stabilize dominant framings under non-scientific governance rules; and (4) artificial intelligence (AI)-mediated access systems that operate as recursive second-order filters by reproducing prestige-weighted patterns through citation bias, over-generalization, and automation bias. The manuscript's incremental contribution is to theorize how AI-mediated synthesis and discovery reshape epistemic authority and legitimacy as workflow infrastructure (not merely as efficiency tools), to make this mechanism explicit in a minimal formal sketch, and to specify safeguards (validation loops, traceability, and oversight) that condition when AI strengthens vs. weakens scientific autonomy. Governance implications are derived from social epistemology and the sociology of quantification rather than presented as normative add-ons, and boundary conditions are sharpened with explicit falsifiers. Finally, the paper proposes a minimal empirical program to quantify agenda alignment, consensus lock-in, and amplification effects in AI-mediated discovery, summarization, and gatekeeping.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1746718</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1746718</link>
        <title><![CDATA[A critical scoping analysis of digital media literacy research in selected Arab and international journals (2016–2025)]]></title>
        <pubdate>2026-03-23T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Mohammad Ali Alquaary</author><author>Osama Abdelrheem Ali</author><author>Hossam Fayez</author>
        <description><![CDATA[IntroductionDigital media literacy has emerged as a critical field of inquiry in response to the rapid expansion of digital communication technologies and social media platforms. Despite the growing importance of this field, scholarly production remains uneven across different academic contexts. This study aims to provide an in-depth analytical examination of contemporary trends in digital media literacy research by analyzing publications in selected Arab and international peer-reviewed journals.MethodsThe study adopts a secondary data analysis approach to systematically examine published research in four peer-reviewed journals—two Arab and two international—over a 10-year period from 2016 to mid-2025. This method, widely recognized in qualitative research, was employed to analyze the theoretical orientations, methodological approaches, and thematic patterns of the selected studies, as well as to identify existing research gaps within the field.ResultsThe findings reveal a clear imbalance in scholarly contributions, with international journals accounting for 66.5% of the analyzed studies compared to 33.5% in Arab journals. The majority of the reviewed research focused on educational and media-related topics (70%), while other themes received comparatively limited attention. Furthermore, the analysis shows that approximately 73% of the studies lacked a clearly articulated theoretical framework, which limits the development of a solid epistemological and philosophical foundation for the field. The results also indicate that research in digital media literacy remains predominantly descriptive, with limited methodological diversity.DiscussionThe findings highlight the need to strengthen theoretical and methodological rigor in digital media literacy research, particularly within the Arab academic context. Developing more diversified theoretical frameworks that incorporate broader social systems is essential for advancing the field. The study also underscores the importance of employing more varied research methodologies, including qualitative and experimental approaches.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1762083</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1762083</link>
        <title><![CDATA[Managing technological sovereignty: a systematic review of semiconductor industry policy and regional ecosystem governance]]></title>
        <pubdate>2026-03-06T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Jingwen Cai</author><author>Xuexian Fang</author><author>Yifen Yin</author><author>Yuanyuan Yu</author><author>Chunning Wang</author><author>Wai In Ho</author><author>Haoqian Hu</author>
        <description><![CDATA[IntroductionAs the semiconductor industry shifts from a logic of efficiency to one of technological sovereignty and supply chain resilience, engineering managers and policymakers face unprecedented uncertainty. While nations are relaunching industrial policies to mitigate geopolitical risks, a critical puzzle remains: why do similar macro-strategies yield divergent outcomes across different regional innovation ecosystems? Existing literature tends to bifurcate between macro-level state competition and micro-level firm strategies, creating a theoretical disconnect.MethodsDrawing on Merton's Middle-Range Theory, this study bridges this gap by adopting a “structure-process-function” perspective. We conducted a systematic review of 104 core articles from the Web of Science Core Collection to diagnose the meso-level governance mechanisms that mediate national strategy and regional context. We propose a “dual fit” analytical framework, arguing that policy effectiveness is contingent upon two simultaneous alignments: (1) “strategy-execution fit” (macro-meso), where governance mechanisms (process) must align with national security goals (structure); and (2) “execution-context fit” (meso-micro), where interventions must be embedded within the region's specific endowments and dynamic capabilities.ResultsOur findings identify two primary failure modes: “governance failure” (misalignment of incentives) and “contextual failure” (neglect of absorptive capacity).DiscussionThis study contributes to engineering management theory by providing a multi-level mechanism to diagnose policy-ecosystem fit, offering actionable insights for managing semiconductor supply chains in a fragmented global order.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1759242</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1759242</link>
        <title><![CDATA[Generative artificial intelligence in the publishing industry: adoption, use, intellectual property, and other challenges]]></title>
        <pubdate>2026-03-02T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Marco Giraldo-Barreto</author>
        <description><![CDATA[Taking as a starting point how generative artificial intelligence (GenAI) works, this text explores the level of adoption of such technology in the publishing sector (in particular for Latin America), shows examples of legislation challenges faced by states and the publishing industry in terms of intellectual property, and the implications of GenAI misuse in the academic publishing context. Finally, it proposes a course of action for a responsible adoption for the publishing chain of value.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frma.2026.1738112</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frma.2026.1738112</link>
        <title><![CDATA[What evidence syntheses reveal about PROSPERO, INPLASY, OSF, the Research Registry, and protocols.io: a meta-research study]]></title>
        <pubdate>2026-03-02T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Raissa Tabosa Ferreira</author><author>Manuela Queiroz Reis</author><author>Maria Heloísa Pignataro Lange</author><author>Maycon Willian Fontes da Costa</author><author>Giovanna Kettlen Mendes Silva</author><author>Anne Beatriz Oliveira Lemos</author><author>Douglas Raphael Lela Dias</author><author>Maria de Lourdes Santos Viana</author><author>Ana Luiza Oliveira dos Santos</author><author>Ana Beatriz Maia Fernandes</author><author>Manuella Rocha Boaventura Pinheiro</author><author>Clarice Wanderley de Sousa</author><author>Kevin Henrique Azevedo Duarte</author><author>Gustavo Vicentis de Oliveira Fernandes</author><author>Carlos Marcelo da Silva Figueredo</author><author>Mario Vianna Vettore</author><author>Marcelo Marotta Araujo</author><author>Fabio Gamboa Ritto</author><author>João Vitor dos Santos Canellas</author>
        <description><![CDATA[BackgroundThis meta-research study examined how protocol registration information is reported in evidence syntheses published from 2020 to 2025 and registered in PROSPERO, INPLASY, OSF, the Research Registry, or protocols.io.MethodsWe analyzed 4,750 studies covering various evidence synthesis types. Data were collected on registry use, reporting transparency, and accessibility features. Statistical comparisons included effect sizes with 95% confidence intervals.ResultsPROSPERO remained the most widely used, with registrations from 70 countries. Among studies registered on non-PROSPERO platforms, more than 90% were found in INPLASY and OSF. Compared with PROSPERO, both registries demonstrated stronger reporting practices, with higher protocol status updates in INPLASY and more hyperlinks in OSF. However, a hyperlink did not always ensure public availability, as several OSF protocols required author authorization. Protocols in PROSPERO were associated with multiple publishers. In contrast, INPLASY protocols were more frequently linked to open-access journals, particularly those published by Frontiers and MDPI.ConclusionAlthough PROSPERO remains the reference registry, INPLASY and OSF are playing an increasingly important role in promoting openness and accessibility. Researchers are encouraged to search multiple registries, especially PROSPERO, INPLASY, and OSF, before starting a new study to minimize the duplication of efforts.Systematic review registrationhttps://www.doi.org/10.37766/inplasy2025.6.0114, identifier INPLASY202560114.]]></description>
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