Purposes To analyze the bibliometric characteristics of journals selected for the Phase II of “Excellence Action Plan of China’s STM Journals” （hereinafter referred to as “Excellence Action”）， explore strategies to promote the high-quality development of China’s STM journals based on these findings. Methods Based on bibliometric indicator data from the 2024 Edition of the Chinese Science and Technology Journal Citation Report （Core Edition） （Chinese journals） and the SCI Journal Citation Report （JCR） （English journals）， we conducted statistical analysis on the Chinese and English journals selected for Phase II of “Excellence Action”（including leading journals and tiered journals）. Additionally， we compared and analyzed the trends in relevant indicators for journals that received funding in the first phase but were not selected for the second phase. Combining project application and evaluation requirements （project planning feasibility， academic influence， editorial team， publishing operations， and dissemination capabilities）， we conducted a comprehensive discussion combining quantitative and qualitative analysis. Findings The overall funding rates for Chinese and English journals selected for the second phase of the “Excellence Action” were 35.1% and 50.4%， respectively. Quantitative indicators such as comprehensive evaluation metrics and impact factor were not decisive factors in determining whether a journal was selected. The selected Chinese and English journals were highly concentrated in the three major administrative units of the Chinese Academy of Sciences， the China Association for Science and Technology， and the Ministry of Education （72.5% for Chinese journals and 78.5% for English journals） as well as in research-strong provinces/cities such as Beijing and Shanghai. Conclusions The selection results of the second phase of the “Excellence Action” reflect a comprehensive orientation that balances quantitative indicators and qualitative assessments. The selection philosophy and practices of the “Excellence Action” provide important insights for China’s efforts to build world-class STM journals. Future high-quality development of scientific journals requires a focus on and strengthening of initiatives such as scholar-led journal publishing， leveraging academic leadership， balancing publication scale with academic quality， enhancing editorial and publishing service capabilities， and strengthening sustainable development and platform construction.

Purposes This study aims to compare the efficacy of three popular AI tools （Doubao， Kimi， and DeepSeek） in proofreading medical journal articles， to provide a reference for their rational application in improving editorial efficiency. Methods A total of 43 final-accepted and author-confirmed research articles published in issues from 1 to 12 of 2024 in the Journal of China Medical University were randomly selected. Using manual proofreading as the gold standard， the proofreading performance of Doubao， Kimi， and DeepSeek was statistically analyzed based on the following indicators： grammar， punctuation， quantities and units， numeral usage， terminology， company names， URLs， and software names. Chi-square tests or Fisher’s exact tests were employed for comparisons. Findings Manual proofreading detected 1718 errors in total. The error detection counts for Doubao， Kimi， and DeepSeek were 287 （16.71%）， 127 （7.39%）， and 505 （29.39%）， respectively， with statistically significant differences among the three tools （P < 0.05）. Pairwise comparisons revealed that DeepSeek significantly outperformed the other two tools， with an error detection rate 1.76 times that of Doubao and 3.98 times that of Kimi. Doubao also performed significantly better than Kimi （P < 0.05）. In the comparison of detection rates across various error categories， DeepSeek achieved significantly higher rates in company names （61.54%）， URLs （92.31%）， capitalization （80.00%）， and software names （100.00%） than both Doubao and Kimi （all P < 0.05）. Conclusions DeepSeek demonstrates an advantage in the breadth of error detection， followed by Doubao， while Kimi exhibits the weakest overall detection capability. DeepSeek’s performance in detecting errors in URLs， capitalization， and software names approached human-level performance， suggesting its potential for practical proofreading applications. However， the total number of errors detected by all three AI tools remained significantly lower than that identified through manual proofreading， indicating that general-purpose AI tools still have limitations and pose potential risks in the proofreading of medical articles. Further optimization is needed to enhance their efficacy in this specialized domain.

Purposes This paper discusses the process of negative chain effects caused by generative artificial intelligence （GenAI） in academic publishing， and explores the regulatory approach of GenAI based on the academic publishing scenario. Methods Based on the risk chain， this paper sorts out the formation process of negative chain effects， analyzes how the commercialization of AI intensifies negative chain reactions from the perspective of commercial interests， and then proposes regulatory approaches by matching risk characteristics with policies and regulations. Findings GenAI has formed four negative chain effects through 11 gradual stages： bad money driving out good money， a cycle of cognitive errors， rampant academic dishonesty， and amplified academic injustice. The commercialization of AI has intensified the negative chain reactions by solidifying the direction of academic production， reshaping the academic research evaluation system， and raising the economic barriers to academic publishing. Conclusions The regulatory approaches of GenAI need to pay attention to scenario adaptation， policy coordination and dynamic adjustment at the same time： first， risk level classification and responsibility allocation should be based on subject scenarios and publishing activity roles； second， effective connection between legal provisions and implementation details should be achieved in the fields of data governance and intellectual property rights； finally， a dynamic governance mechanism based on sandbox testing and triggered updates should be established.

Purposes This study monitors the global development of open access （OA）， studies the rationality of article processing charges （APCs）， and analyzes trends in research funding flows， with the aim of providing insights and recommendations for the management of research funding and the promotion of high-quality scholarly communication in China. Methods Based on SCIE data and OA journal websites， we conducted a statistical analysis of global OA journal publishing， authorship， APCs， as well as Chinese authors’ OA publications and APC expenditures. Findings In 2024， a total of 8604 global OA journals and 246 Chinese OA journals were indexed in SCIE， respectively representing year-on-year increases of 10.96% and 14.95%. The global OA publication volume and the publication volume of Chinese OA journals respectively rose by 11.46% and 19.85%. Publications by Chinese authors accounted for 30.09% of the global OA output. Chinese authors published approximately 313500 OA papers across more than 6276 OA journals， with total APC expenditures of about 6.474 billion Yuan（RMB）， increasing by 13.83% in publication volume and 22.38% in APC spending compared with 2023. The National Natural Science Foundation of China （NSFC） remained the primary funding source supporting OA publications by Chinese authors， with its annual APC expenditure rising by 13.31%. Conclusions Both globally and in China， the volume of open access （OA） publications and the total APC expenditures continue to rise， becoming increasingly concentrated in a few major international commercial publishers. To address this trend， it is recommended to reform the academic evaluation system， promote the development of local open access platforms， establish clear open publishing regulations， and strengthen institutional management of the entire publication process.

Purposes By analyzing the current status of clustered development， exploring the path of intensification， this study aims to address the issues of dispersion and homogeneity of Chinese scientific journals， providing references for the top-level design， large-scale development， and personalized construction of Chinese scientific journal clusters， and enhancing the brand influence and resource integration capabilities of building world-class scientific journals. Methods Using the questionnaire survey method， based on the data of 63 domestic scientific journal clusters， the characteristics of the establishment stage， the type of operating units and sources of funds， the scale and language distribution of journals， the operation and management model， academic influence， exploration of academic services， international cooperation， and the construction of digital publishing and new media platforms of the clusters were summarized and analyzed. Findings The challenges faced during the development process were summarized， such as clustering without intensification， lagging digital publishing platform construction， unclear talent team structure and training models， single financing channels， and unclear business models， and development suggestions were provided. Conclusions The clustered development of Chinese scientific journals has achieved remarkable results， with accelerated improvement in scale and quality. However， structural problems such as regional and disciplinary imbalances are prominent， which require further discussion among peers.

Purposes To conduct a cross-sectional evaluation of the AI-generated content （AIGC） detection capabilities of iThenticate， Wanfang Wencha， CNKI， and Jianziyuan AIGC detection tools on medical abstracts， aiming to provide a reference for the appropriate application of such tools. Methods A total of 518 randomly selected review article abstracts served as the original text group. Corresponding AI-polished and AI-generated text groups were created. The three sets of texts were assessed by the four detection tools for AIGC writing identification. Detection results were recorded， and accuracy， consistency， sensitivity， and specificity were calculated. Findings The results showed significant differences among the tools in terms of result presentation and decision thresholds. iThenticate exhibited limited applicability for Chinese texts， while domestic AIGC detection tools demonstrated higher detection rates for fully AI-generated texts but showed notable false positives and false negatives for AI-polished texts， with low consistency and sensitivity. Conclusions AIGC detection can be employed as an auxiliary technical tool for risk alert and manuscript triage， and should be integrated with author disclosure policies， editorial judgment， and industry standards to collectively uphold academic integrity and publishing order.

Purposes To standardize academic publishing and accelerate the dissemination of preprints， and to standardize the citation format of preprints. Methods Conduct online research to sort out the citation formats of journal articles and preprint platforms， and compare and analyze the standards related to preprint citation at home and abroad. Findings The survey found that the citation format of preprints in scientific journal articles is not consistent with the recommendations of preprint platforms， and there is an urgent need to standardize the citation format of preprints. Conclusions Preprints should be included as an independent literature type in the existing reference citation standards. The citation elements should fully reflect the characteristics of preprint resources， adopt existing international standard formats， and be arranged in accordance with reference citation standards.

Purposes To explore the current status of ethical guidelines regarding artificial intelligence generated content （AIGC） on the official websites of Chinese medical journals， and propose strategies for improving the ethical review system of AIGC. Methods A total of 421 medical and health journals indexed in the World Journal Clout Index（WJCI）of Scientific and Technological Periodicals（2024） were included in the study. The official websites of these journals were reviewed to assess the ethical guidelines requirements regarding AIGC. Findings Among the 421 medical journals included in this study， only 100 （23.8%） had established specific ethical requirements for AIGC， highlighting a pervasive lack of awareness of ethics. There were significant differences in the ethical guidelines requirements for AIGC across journals with different publication languages， primary sponsors， and indexing databases （categories and quantities）. The development of ethics in Chinese-language journals is less developed than that of English-language journals. The statements regarding AIGC ethical guidelines in these journals are unclear in terms of scope definition， disclosure requirements， and authorship policies， indicating a lack of adequate attention to the establishment of ethical review systems for AIGC. Conclusions Chinese medical journals should enhance the supervision of AIGC ethical guidelines by implementing a trinity framework of “policy regulation + technical safeguards + accountability statements”， and bolstering editorial training， prioritizing official website development， thereby facilitating the establishment of comprehensive review systems for AIGC ethics.

Purposes To explore the development path and framework of AI-driven transformation for scientific journals’ knowledge service， and provide practical references for intelligent knowledge services in scientific journals. Methods Firstly， we conducted a systematic analysis of the characteristics and evolutionary process of the intelligent knowledge service of scientific journals. Next， we presented a case study of the STM publishing industry， and then analyzed the deployment of AI policies， the integration of AI technologies and the development of AI applications. Finally， we provided an AI application strategy for scientific journals. Findings We proposed a four-dimensional framework of “Scene-Resource-Model-Policy” for the intelligent knowledge service of scientific journals. Specifically， this includes： constructing a trustworthy AI corpus based on journal resources， guaranteeing model professionalism based on domain knowledge and publishing standards， developing scenario-driven AI applications， and improving the AI governance system of scientific journals. Conclusions AI for Science （AI4S） is accelerating the transformation of scientific journals into intelligent knowledge services. The STM publishing industry should develop AI strategies， and establish feasible implementation plans.

Purposes The purpose is to summarize experiences and lessons for the transformation and development of academic publishing institutions in China，through systematic and in-depth research into the business model transformation of the five major international academic publishing groups. Methods An integrated methodology combining SWOT analysis， case study research， and inductive summarization was systematically employed. Findings The five major international academic publishing groups actively participate in open access publishing， continuously integrate scientific research platforms and tools， collaboratively upgrade to knowledge solution providers， and proactively explore the deep integration of AI with high-quality resources， thereby seizing opportunities in the transformation of academic publishing， strengthening market influence， cultivating new revenue avenues， and developing emerging business segments. Conclusions China’s academic publishing institutions should seize the opportunity presented by the development of open access journals to actively merge into the transformation of open access scholarly ecosystem； strengthen business diversification to extend the academic publishing industry chain； integrate platforms as a key component to advance journal cluster development； and proactively explore the application of AI tools to extract the full potential value of academic resources.

Purposes To address the limitations of commercial proofreading software， as well as systematic shortcomings in the current generative artificial intelligence （GAI） workflow， this study developed an agent tailored to copyediting-proofreading needs， in order to enhance the efficiency and quality of editorial work. Methods The agent called Déjà Lu （meaning “already read” in English） was independently developed using Python and JavaScript， and its performance was evaluated through a combination of quantitative analysis and questionnaire-based user surveys. Findings The Déjà Lu agent innovatively realizes automated and intelligent processing of proofreading， polishing， and translation for academic manuscripts， with an average processing speed of about 0.007 seconds per character. The proofreading function improved the error calibration rate by an average of 15%， with overall processing time varying across tasks. The polishing function enhanced language expression quality by an average of 7%， while the total processing time was significantly shortened by up to 92%. The translation function achieved good accuracy， with its overall processing time markedly reduced by up to 83%. Editors and authors generally gave positive evaluations of the agent’s performance. Conclusions The modular， hierarchical， and user-friendly Déjà Lu agent ensures strong maintainability， reusability， and extensibility. It can be effectively integrated into editorial workflows to enhance efficiency， providing a new tool and practical paradigm for the integration of artificial intelligence （AI） and academic publishing.

Purposes To conduct an in-depth analysis of the current status of generative artificial intelligence （AI） usage policy and regulation construction in information resources management journals both domestically and internationally， providing references and inspirations for the development of domestic information resources management journals. Methods Taking the journals in the field of information resources management （information science & library science） indexed by CSSCI and JCR （SCIE and SSCI） as the research samples， and based on the criterion of whether they have released policies on the use of generative AI， 61 journals were finally selected for analysis. The grounded theory method was employed to sort out and analyze the generative AI usage policies and regulations they have issued. Findings Most of the international journals in the field of information resources management belong to large international publishing groups， and their policies and regulations on the use of generative AI are basically systematic. In contrast， domestic journals in this field are still in the initial stage of formulating policies on the use of generative AI， lacking systematic and structured governance policies for generative AI. Conclusions Domestic journals in the field of information resources management should actively embrace the trend of generative AI application， fully leverage the strengths of all parties， and enhance their governance capabilities and effectiveness in generative AI. From a macro-strategic perspective， the development of the information resources management discipline and the governance concepts of related industry fields should be utilized to empower the governance of generative AI in field journals. From a micro-implementation perspective， each journal should formulate generative AI application policies that conform to the characteristics of the discipline， set up clear policy sections on their websites， clarify the norms and boundaries of generative AI usage， establish measures for handling violations， and retain the discretionary power of the journal.

Purposes To evaluate the diagnostic efficacy of generative artificial intelligence （GenAI） in detecting statistical results errors in biomedical journals. Methods A convenience sampling method was used to include 21 positive samples （with statistical errors in results） and 21 negative samples （without statistical errors in results）. Kimi and DeepSeek（both deep thinking and non-deep thinking modes） were employed， combined with three prompting strategies ［direct questioning， literature reference （single or dual-paper jointly）， and terminology prompting （full-text review or table-by-table review）］ to form 10 combined strategies. The diagnostic performance of these ten strategies was compared. Findings DeepSeek deep thinking demonstrated the highest sensitivity （47.6%～100.0%） and accuracy （71.4%～90.5%） across all prompting methods， followed by DeepSeek non-deep thinking， with Kimi performing the worst. Literature reference and specialized terminology prompting methods yielded superior sensitivity and accuracy compared to direct questioning within the same GenAI model. “DeepSeek deep thinking + literature reference （dual-paper jointly）” achieved the highest accuracy （90.5%）， while “DeepSeek deep thinking +terminology prompt （table-by-table review）” yielded the highest sensitivity （100.0%） and most comprehensive error detection. Except for “DeepSeek deep thinking + terminology prompt” （specificity： 85.7% for full-text review， 71.4% for table-by-table review）， all other combination strategies demonstrated specificity above 90%. Conclusions DeepSeek deep thinking demonstrated superior performance in detecting statistical errors in biomedical journals. Literature reference and terminology prompting were more effective than direct questioning， though false positives occur. It is recommended that editorial offices adopt “DeepSeek deep thinking+literature reference （dual-paper jointly）” for initial manuscript screening， and “DeepSeek deep thinking+terminology prompt （table-by-table review）” for detailed statistical verification of flagged manuscripts and unpublished studies， issueed by manual validation.

Purposes This study aims to analyze the characteristics of Chinese scientific journals indexed in the Emerging Sources Citation Index （ESCI）， which is expected to serve as a reference for improving the international dissemination of Chinese scientific journals. Methods Multivariate statistical， data analysis were used to study the 14 ESCI-indexed Chinese scientific journals. To be specific， data on the basic journal information， English content presentation， international topic selection， and citation index were collected. Findings Through improving the English-language websites and metadata presentation， achieving indexing in multiple international databases， 14 journals have initially established an international dissemination system. However， they still face challenges in terms of global academic impact， such as high self-citation rate， a significant proportion of zero citation papers， and low discipline ranking. Conclusions On the premise of a clear orientation， Chinese scientific journals should made appropriate strategies. Journals with a certain level of international visibility should focus on international topic selection and international academic activities， increase publication and provide bilingual publishing when necessary. Journals lacking international visibility should strengthen English content presentation， join cluster platform， and apply for index by international databases.

Purposes This study explore the application effect of the intelligent proofreading system and provide a reference for the selection and application of intelligent proofreading software in the editing and proofreading process of scientific and technological journals. Methods A combination of machine reviewing and manual reviewing was adopted to analyze the review and proofreading results of the papers in 4 issues of Progress in Geography by the CNKI （China National Knowledge Infrastructure） Intelligent Proofreading System. The review and proofreading results were classified into three types： Effective， general and ineffective results based on the criteria of necessary， optional， and impossible correction. The percentages of effective results and ineffective results were calculated. And the common cases of effective and ineffective results were summarized. Findings The percentages of effective results and ineffective results were 32.3%，22.1% respectively. The CNKI Intelligent Proofreading System has a significant advantage in detecting simple errors in grammar， handwriting， and terms related to administrative divisions， while a deficiency in understanding professional terms based on the context of the article. Conclusions The CNKI Intelligent Proofreading System can effectively enhance work efficiency， but it cannot completely replace manual editing and proofreading. The combination of intelligent proofreading and manual review is necessary to enhance the efficiency and quality of editing and proofreading.

Purposes To establish a comprehensive digital intelligence platform covering the entire workflow， enhance the publication quality and academic influence of the Journal of Beijing University of Posts and Telecommunications， and drive the journal’s digital transformation and ecological restructuring. Methods Based on the practical experience of the Journal of Beijing University of Posts and Telecommunications， this study centers on seven core dimensions—"author-reader-editor-reviewer-article-research-popularization" as the core， we developed seven major systems. By integrating advanced technologies such as data visualization， multimodal fusion， blockchain-based evidence storage， natural language processing， artificial neural networks， embodied intelligent interaction， multi-agent collaboration， machine learning-driven algorithms， and ultra-long video generation， we achieved automated and intelligent control across the entire chain： "creation-review-processing-output-audience-dissemination-evaluation". Findings Utilizing this platform reduced review cycles by 30%， lowered editorial error rates by 25%， increased academic misconduct detection to 98%， and decreased innovation assessment errors by 15%. This significantly enhanced publishing efficiency and content quality， forming a collaborative ecosystem of "data-driven decision-making and dynamic optimization". Conclusions This digital-intelligence platform not only provides the Journal of Beijing University of Posts and Telecommunications with an end-to-end intelligent solution but also offers a replicable model for the transformation and upgrading of China’s scientific journals. It provides crucial theoretical and practical support for cultivating world-class journals.

Purposes Based on a comprehensive synthesis of expert presentations delivered at the Fourth Symposium on Sci-Tech Journal Development， this study reviews the topic from a full research-lifecycle perspective. It systematically examines the current applications of artificial intelligence （AI） in academic publishing， identifies the key challenges encountered in practice， and analyzes potential future trajectories， with the aim of providing a reference for advancing the high-quality development of sci-tech journals empowered by AI. Methods Presentations from 15 experts were thematically clustered and reviewed according to 3 categories： current practical applications， challenges and countermeasures， and future development directions of AI in sci-tech journals. The key viewpoints of experts were reviewed from the perspective of the entire research process. Findings AI has been comprehensively applied across all stages of sci-tech journal publishing， including writing， manuscript evaluation， editorial processing and proofreading. However， challenges related to academic ethics， publishing quality， data security， and research fairness have emerged. Major publishers and some individual journal editorial offices had established AI usage guidelines， addressing potential risks by clarifying application boundaries and implementing disclosure mechanisms. Conclusions The development and adoption of AI had deeply permeated scientific research and academic publishing. Academic journals should actively embrace and systematically plan for AI integration by advancing its application across 6 dimensions： attitude， technology， system， team， service， and ethics.

Purposes To analysis development of scientific journal clusters and digital journal group publishing platform and propose a clear direction for future endeavors. Methods Adopting literature and data research methods， to search and collect relevant research results and materials to sort out the related policies as well as beneficial measures for the practice exploration of scientific journal clusters and digital journal group publishing platform， and its character during nearly 20 years. Findings Since the beginning of this century， with the promote of the policy of enterprise system reform and publishing digital transformation as well as the financed with the fund of such as China Science and Technology Journal Excellence Action Plan， a series of professional journal clusters of scientific journals（such as Beijing Zhuozhong Publishing Co.， Ltd，Optics Journal Union and Chinese Medical Association） and its digital publishing platform greatly developed. In addition， comprehensive scientific journals clusters digital publishing platform（such as SciEngine， SciOpen） have been self-construction independently. And all these will be helpful to the future with continuously developing and improving in these years. Conclusions A series of scientific journal clusters and digital journal group publishing platform have been built in these years and should further improve in following years.

Purposes This paper investigates the current situation of the dissemination of popular science journals on WeChat video account and analyzes the problems existing in WeChat video account in the short video media based on the AARRR model， and proposes countermeasures to provide references for the dissemination practice of popular science journals on WeChat video account in China. Methods Taking 21 excellent popular science journals as samples， the opening situation， operation situation， content release situation and existing problems of their WeChat video accounts were analyzed. Findings There are the following problems in the WeChat Video Accounts of popular science journals： insufficient integration awareness between journals and video accounts， similar front-end scene construction， inversion of quantity and quality， weak awareness of brand value transformation， and lack of emotional connection. Conclusions The WeChat video accounts of popular science journals should take “users” as the core and accelerate the construction of a theoretical model with “channel power - scene power - marketing power - influence power - dissemination power” as the framework， in order to improve the dissemination effect of WeChat video accounts of popular science journals and promote the win-win situation of popular science journals and WeChat video accounts.

Purposes To conduct a comprehensive examination of the employment quality of the scientific journal editor group and propose effective measures to address employment changes accordingly. Methods This study adopts a dual macro-micro analytical framework. It collected and analyzed data from the “Human Resources” section of the China Scientific Journal Development Blue Book （2020—2024） and conducted a nationwide large-scale questionnaire survey and analyzed 1058 valid responses. Findings The findings reveal that the employment scale of scientific journal editors remains stable， with diverse post types， highly qualified practitioners， multifaceted career development pathways， and strong professional commitment， indicating overall favorable employment quality. However， potential risks in the internal labor market of the industry and the strong impact of generative AI technology have created certain obstacles to improving the employment quality of editors. Conclusions First， accelerate the innovation of AI application scenarios to empower the employment ecosystem of scientific journals； second， enhance the digital skills of editors and carry out categorized human capital investment； third， dynamically update occupational classifications and explore the layout of human-machine collaboration. These measures aim to promote the formation of an employment pattern in the entire scientific journal industry with efficient matching， sufficient opportunities， sound protection， and smooth mobility.

Purposes This study aims to comprehensively analyze the content， channels， and models of author services provided by the four leading international academic publishing groups （Elsevier， Springer Nature， Wiley， Taylor & Francis）， offering references and insights for Chinese scientific journals to establish and improve their author service systems. Methods Through web-based research， content analysis， and comparative study methods， this study systematically examined the author services offered by the four leading international publishing groups across the entire publishing process（pre-submission， during publication， and post-publication）and conducted a multi-dimensional comparison to summarize the commonalities and distinctive features of their author services. Findings The four leading groups have established a full-chain service system covering “pre-submission guidance-publication support-post-publication promotion”. Before submission， they provide systematic academic training， intelligent journal selection tools， and in-depth editorial services. During publication， they enhance efficiency and author experience through online proofing systems， preprint platforms， rapid manuscript transfer mechanisms and manuscript status tracking services. After publication， they expand dissemination and strengthen author engagement through content sharing， multimedia promotion， and impact tracking. Conclusions Chinese scientific journals should learn from international best practices by constructing a full-process service system， strengthening digital platform development， accelerating the application of artificial intelligence， deepening humanistic care， and advancing open access to enhance their international competitiveness.

Purposes To explore the dynamic changes in the distribution of authors' countries and regions in Chinese social science journals included by SSCI from 2020 to 2024， and to provide reference data for enhancing the international development of Chinese journals included by SSCI. Methods Using the InCites database， we collected metrics and publication information for all papers published in Chinese SSCI-included journals from 2020 to 2024. We further compared the number of papers published and the average citations per paper from different countries and regions in 15 Chinese SSCI journals from 2020 to 2024. Findings From 2020 to 2024， there were significant differences in the number of papers published and the proportion of international collaboration papers （11.85%～47.80%） among the 15 SSCI-included journals in China. From 2020 to 2024， scholars from 97 countries and regions published academic papers in China’s 15 SSCI-included journals. Among them， Chinese scholars contributed the most papers to China’s SSCI-included journals， with their contribution rate increasing year by year， but their average citations per paper were below the overall average. Excluding China， American scholars have the highest contribution to China’s SSCI-included journals， but their share of publications has been decreasing year by year； scholars from W7 countries have seen a steady increase in the number of publications and the highest average citations per paper. In all papers published annually from 2020 to 2024， the number of publications and contribution of foreign authors in China’s SSCI-included journals have shown a decreasing trend year by year. Conclusions In the future， Chinese journals included in the SSCI should actively seek out high-quality papers from renowned scholars in the field of social sciences abroad， while ensuring a steady supply of high-quality domestic manuscripts. They should also participate in international academic conferences to enhance their visibility and attract submissions from foreign scholars. These efforts will promote the high-quality development and internationalisation of Chinese journals included in the SSCI.

Purposes This paper summarizes the development strategy， operation， and cooperation of African journals online （AJOL）， providing reference for the journal development. Methods Taking the AJOL platform as a case study， it makes a comprehensive analysis of the developing road， technical measures， operation mode， international cooperation， and so on， by analyzing the website data and combining with the literature of African research. Findings Until now AJOL has gathered 894 journals by taking unified technical standards， and actively expanding international cooperation as development measures. Among them， there are 424 kinds of open access （OA） and 92 journals included in the Scopus database. AJOL platform promotes capacity building and standardization in the development and cooperation. It evaluates journals according to its own JPPS grading system to promote the self-construction of journals. The diversified operation mode of AJOL is worthy of reference for the construction of journal platforms in China. Conclusions AJOL platform’s standardized management and the leading role in promoting international cooperation have greatly promoted the common development and international influence improvement of African journals.

Purposes To systematically evaluate the effectiveness of large language models （LLMs） in editing English abstracts for medical journals and to distill actionable editorial workflow guidance. Methods We selected 100 paired Chinese-English abstracts from 11 representative medical journals and established a five-dimensional evaluation framework （terminology， grammar， conventions， expression， and logic）. We compared the automatic error-detection performance of three LLMs， followed by double-blind human adjudication. Findings The three LLMs collectively identified 1197 issues， of which 1113 （93%） were confirmed after double-blind review. Detected problems were dominated by terminological accuracy （408， 36.7%）， followed by idiomatic expression， grammatical correctness， and adherence to academic conventions. Different LLMs exhibited complementary strengths across evaluation dimensions； integrated use can enhance overall abstract quality. Conclusions LLMs are effective assistive tools for English-abstract editing in medical journals， improving editorial efficiency and textual quality. Recommended practices include refined prompt design， multi-model integration， structured assessment frameworks， clear human–machine role boundaries， and strict adherence to ethical standards.

Purposes In order to fundamentally prevent academic misconduct before it forms， a pre-positioned， systematic and intelligent graded risk early warning system of academic misconduct is constructed based on digital intelligence technology and subject cross-linking. Methods The cognitions and requirements of academic researchers， publishing institutions， regulatory authorities， etc. on academic misconduct governance are collected by applying interview method； the practical progress of early warning of academic misconduct is understood through online retrievals and telephone surveys， etc.； the key factors of early warning of academic misconduct are identified by applying Delphi method to collect expert opinions； and the graded risk early warning system of academic misconduct is constructed and the implementation mechanism is analyzed by applying logical analysis method. Findings The graded risk early warning system of academic misconduct is designed innovatively， and the early warning index of risk of academic misconduct （RAM） is constructed to measure the likelihood of academic misconduct. Based on three main subsystems of data collection， analysis and judgment， and real-time warning， the process warning， negative warning， and interactive warning can be achieved through cross-platform adaptive embedded modules. Hence， the budding academic misconduct activities can be avoided collaboratedly facing the various aspects of academic research， the entire process of academic publishing， and the multi field of academic ecosystem. Conclusions The graded risk early warning system of academic misconduct is a pioneering research and prospective exploration that shifts the prevention threshold forward of academic misconduct， which can promote the fundamental transformation of academic misconduct governance from post punishment to pre prevention， so as to effectively maintain academic integrity， and ensure the healthy development of academic research.

Purposes Based on the theory of multimodal discourse analysis， this study explores the communication strategies of Chinese English-language scientific journals on international social media platforms. It analyzes the presentation forms， construction logic， and dissemination mechanisms of their multimodal discourse， aiming to provide theoretical support and practical reference for enhancing the international communication power of these journals. Methods Using Python， 120 top-liked posts （top 60 each） from X and Facebook during January 1-31， 2024 were collected. Content analysis and multimodal discourse analysis were applied in combination. Findings In the visual modality， research outcome–oriented content accounted for the largest proportion， with the most common format being “text + image”. In the textual modality， declarative sentences and specialized terminology were frequently used to ensure academic precision. Conclusions At the cultural level， it is essential to promote the integration of “technology and culture” to enhance visual communication efficacy. At the contextual level， strategies should be tailored to different media platforms to strengthen issue responsiveness. Regarding content， a multimodal collaborative dissemination matrix should be constructed. In terms of expression， optimizing the linguistic system and establishing a unified visual identity system are crucial.

Purposes This paper aims to provide guidance on optimizing the peer review process in cross-disciplinary journals. Methods This research uses a case study and comparative analysis to examine the optimisation of the review process for the Industrial Engineering Journal. It investigates the introduction of a pre-review mechanism by area editors， establishing a four-stage process involving “editorial preliminary screening， area editor pre-review， double-blind expert review and editor-in-chief final review”， along with a supporting quality control system. The effectiveness of this mechanism is validated by comparing its key performance indicators， such as average review cycle times. Findings This novel process has established a professional team of area editors and clarified their responsibilities for providing professional assessments， evaluating manuscripts’ innovative value， and suggesting appropriate reviewers. Additionally， the journal has implemented a quality control system incorporating timeliness management， standardized feedback， and performance evaluation. Consequently， the average review cycle has decreased from 149 days to 68 days. Conclusions In practice， the area editor pre-review mechanism has proven to be efficient. The process has been developed to balance review efficiency， academic quality， and fairness， thus offering a replicable model for optimizing the review process of interdisciplinary journals.

Purposes Scientific papers published at home and abroad by Chinese researchers are analyzed to provide decision-making support for management personnel at all levels. Methods Based on important databases， such as Science Citation Index （SCI）， Ei Compendex， Conference Proceedings Citation Index-Science， Social Science Citation Index， and Chinese Scientific and Technical Papers and Citations Database， the number of Chinese excellent scientific papers， scientific papers， discipline and region distribution， papers of fund projects， international coauthored papers， and Chinese social science papers were studied and analyzed. Findings The number of excellent scientific papers in China was 759.5 thousand in 2024， up 9.1% from 2023. According to SCI， Chinese authors have produced about 866.8 thousand papers indexed by SCI in 2024. Chinese papers were cited 96.79 million times from 2015 to August 2025， increasing 20.0% compared with that in 2024. China ranked the second. On average， Chinese papers were cited 17.24 times per paper， increasing by 6.4% compared with that in 2024， exceeding the world average of 16.43 times per article. Conclusions The number of Chinese scientific papers and the improvement of paper impact has increased rapidly in recent years.

Purposes This study aims to depict the current international landscape of open peer review （OPR） practices in academic journals through a large-scale survey and analysis. It seeks to identify the basic characteristics of journals adopting OPR， as well as the extent and strategies of OPR implementation. The goal is to provide an empirical reference for the adoption of such practices by journals in China. Methods The study is based on a sample library of 1111 OPR journals and 11110 articles published within them. An investigation and statistical analysis were conducted on their publication information， academic impact， and specific practices concerning the openness of the review cycle， reviewer identities， and review content. Findings The results indicate a high concentration of OPR practices in Europe， within commercial publishing houses， and in the disciplines of medicine and health sciences. The adoption of OPR is primarily led by non-top-tier journals， with active participation from journals of medium to low impact. The implementation strategies for different open elements vary. The core dimensions of OPR， namely signed reviewer identities and open peer review reports， are adopted less frequently and are predominantly based on a voluntary model. Furthermore， there is a significant lack of standardized practices， such as assigning DOI to peer review reports. Conclusions OPR practices are characterized by strategic and differentiated adoption， with a conservative approach towards opening up the core elements of the review process. The heavy reliance on a voluntary model limits the potential for increasing the degree of openness. To advance OPR from a superficial to a substantive practice， journals need to progressively enhance the openness of core review stages. It is also crucial to explore effective incentive mechanisms to increase the willingness of authors and reviewers to participate in these core open practices， thereby overcoming the limitations of the voluntary model.

Purposes This paper explores the systemic impact of generative AI （GenAI） on the research ethics framework. It moves beyond the traditional “morality” perspective， which focuses on individual academic misconduct， to an “ethics” perspective characterized by collectivity. It analyzes the profound challenges arising from AI's reshaping of the research interaction environment. Methods The paper employs a methodology combining conceptual analysis and case studies. It first deconstructs the three core assumptions underpinning traditional research ethics： the singularity of the violator， the attributability of misconduct intention， and the stability of ethical norms. Second， by analyzing cases such as “prompt injection” and “writing style homogenization”， it demonstrates the profound shifts in the research ethics ecosystem caused by GenAI. Findings GenAI is systemically challenging the three core assumptions of the traditional ethical framework： First， ethical subjects are shifting from “rule followers” to “strategic actors”. Second， ethical risks are expanding from “individual intentions” to “unattributable emergent consequences”. Third， ethical norms are evolving from “static rules” to a “dynamic field”. The governance paradigm centered on preventing individual misconduct is gradually becoming ineffective， and the academic community is entering a phase of social experimentation to seek a new ethical compact. Conclusions The academic community should abandon a “containment” mindset and construct a new ethical compact within this dynamic social experiment. As key nodes in the research ecosystem， scientific journals must shift their governance strategies from preventing individual misconduct to nurturing the systemic ecosystem. It is suggested that the journal community explore three paths： establishing verifiable procedural rules to rebuild trust； moving beyond single-paper compliance checks to safeguard the diversity of the collective knowledge ecosystem； and building an evolving governance framework to lead the community in adapting to the uncertain technological environment.

Purposes To analyze the structural characteristics of papers from high-impact ophthalmology institutions in China， providing empirical evidence for formulating development strategies for Chinese-language medical journals. Methods High-impact ophthalmology institutions in China were identified based on the SCIE database， and their domestic influence was verified using the Chinese Hospital Specialty Reputation Rankings. Comparative analysis was conducted on publication trends of these institutions in Chinese/English-language ophthalmology journals across indexed databases from 2010 to 2024. Findings The overall output of ophthalmology research papers in China remained relatively stable， but a severe outflow was observed： the number of papers published in SCIE journals increased annually， while both the total number of papers and the average number of papers per Chinese-language journals showed a downward trend. High-impact institutions continued to expand their research output， yet their papers in Chinese-language journals gradually decreased； however， their proportion in the total papers of Chinese-language journals showed an upward trend. These characteristics were more prominent in Chinese core journals. Conclusions The outflow of ophthalmology research papers in China continues to intensify， with the shrinking speed of Chinese-language journals in paper quantity exceeding the rate of source loss from high-impact institutions. To address this， it is necessary to strengthen policy implementation supervision， reconstruct the journal evaluation system， build independent data platforms， and guide journals to return to their original mission of serving domestic needs.

Purposes This study aims to analyze the usage norms of generative artificial intelligence （GenAI） in academic journals of the social sciences in China， offering guidance for the news and publishing industry to establish academic publishing standards that prevent unethical practices caused by GenAI tools. It also provides references for academic journals to formulate and improve GenAI usage norms， thereby advancing the high-quality development of academic journals. Methods We investigate the usage norms of GenAI in academic publishing among the 660 social science journals indexed in the CSSCI （Chinese Social Sciences Citation Index， from 2023 to 2024）. By employing the BERTopic model for thematic identification， it aims to understand the characteristics of GenAI usage norms and their focal areas in academic journals. Findings The findings reveal that only 7.58% of social science journals in China have established GenAI usage norms in academic publishing， with significant differences across disciplines. The publication of these norms by journals lacks standardization， resulting in authors， editors， and reviewers often being unable to access timely requirements regarding GenAI usage. Moreover， existing norms impose requirements more clearly on authors than on editors and reviewers， and the specific provisions of these policies have not yet reached a consensus. Conclusions To address these issues， social science journals should develop and refine GenAI usage norms in academic publishing across four dimensions： journals， authors， editors， and reviewers. At the journal level， measures include promptly establishing and improving norms based on real needs， publishing norm content through multiple channels， enhancing training for reviewers and editors in identifying GenAI-generated content， actively developing or adopting AIGC detection services， and establishing a tiered penalty system for violations. For authors， principles such as reasonable use， prohibited use， accountability， disclosure requirements， and penalties for violations should be clarified. For editors， principles including prohibiting GenAI involvement in decision-making， AIGC detection， and intellectual property protection should be established. For reviewers， principles like confidentiality， independence， transparency， and oversight should be clearly defined.

Purposes This study investigates the policy frameworks of library and information science （LIS） journals regarding the use of generative artificial intelligence （AIGC）， aiming to provide insights for the standardized application of AI technologies in LIS scholarly publishing and scientific research. Methods A multi-dimensional analysis was conducted on SSCI- and ESCI-indexed LIS journals using a mixed-methods approach combining LDA topic modeling and Nvivo-based qualitative coding， examining structural dimensions （indexing status， journal quartile， publisher background） and role-specific dimensions （authors， reviewers， and editors）. Findings AIGC policies exhibit significant differentiation across indexing databases， journal rankings， and publishers. Author-related policies primarily focus on writing assistance and data analysis， while policies for reviewers and editors emphasize confidentiality， integrity， and accountability. Although some journals have established governance measures， challenges persist， including uneven policy coverage， hierarchical disparities， and ambiguous boundaries of acceptable use. Conclusions Current AIGC policies in LIS journals lack a comprehensive governance system. A tiered framework of “top level standard setting-mid level platform support-grassroots implementation and enforcement” should be established to promote the synergistic advancement of technological empowerment and research integrity in scholarly publishing in the AI era.

Purposes The aim of this study is to systematically investigate how academic journals in China’s humanities and social science fields respond to artificial intelligence generated content （AIGC）， to reveal the phased characteristics of policy development， the core consensus and the practice differences， and to provide recommendations for improving the governance ecosystem of AIGC in these journals. Methods A three tier analysis framework of normative strategies for academic journal AIGC was proposed. Using a mixed methods approach that combined manual coding with large language model techniques， we quantitatively assessed the AIGC usage policy texts of 106 Chinese humanities and social science journals. Findings The study found that the development of AIGC norms in Chinese humanities and social science journals follows a phased pattern， from an initial response in early 2023 to routine follow up by 2025. The current policy system has established a consensus framework centered on author agency， reflected mainly in four areas： identifying the sole responsible author， prohibiting AI authorship， positioning AIGC as an auxiliary tool， and establishing basic handling measures. However， in practice there is differentiation， manifested by varying depths of information disclosure， differences in review methods and the intensity of handling measures， and a limited scope of normative subjects. Conclusions Domestic humanities and social science journals’ AIGC policies are progressing from basic norms toward systematic governance. Future efforts should aim to build a process governance system that covers the entire academic publishing workflow and involves multiple stakeholders， promote a proactive and standardized review mechanism， enhance the depth and traceability of information disclosure， explore integrating AIGC governance effectiveness into journal evaluation systems， and develop differentiated discipline norms， thereby fostering a healthy and sustainable academic publishing ecosystem.

Purposes Explore the boundaries of rights and responsibilities between publishers and data users in AI model training scenarios， providing a reference for improving copyright protection and circulation rules for scientific journal-published data in China. Methods This study examines stakeholders in scientific journal publishing，including publishing institutions， industry associations， and government bodies，by analyzing publicly available copyright agreements， technical terms， position statements， policies， and regulatory documents. It aims to characterize the features of copyright clauses， technical restrictions， and obligation terms in these materials. A case study approach is adopted， focusing on the practices of five leading publishing institutions： Elsevier， Springer Nature， Sage， Wiley， and Taylor & Francis. The study compares copyright infringement risks associated with different models of using published data for AI training. Findings At the scientific journal level， copyright transfer agreements for AI model training have yet to be substantially updated. As key players in protecting published data copyrights， leading publishers have only a small number of copyright agreements explicitly address various scenarios related to AI training. Publishing institutions may adopt technical restriction clauses， maintain a neutral stance， or implement data management models better suited for AI training scenarios. Publishers with a higher proportion of open-access papers are more inclined to proactively provide data access. When utilizing published data for AI model training， publishing institutions typically adopt two strategies：independent development under “internal fair use” provisions or licensing content to third parties. These two approaches differ in terms of data circulation scope. The key copyright boundary disputes involved are the definition of “fair use” and completeness of the authorization chain. Conclusions To address the needs of AI model training， copyright agreements should be supplemented with provisions clarifying the applicability of “fair use” or adding “sublicensing” clauses. Publishing institutions should develop copyright management frameworks tailored to AI training requirements. Data holders in publishing institutions and AI model developers must clearly define data permissions and delineate responsibility boundaries， thereby providing a foundation for establishing market-oriented mechanisms for data circulation revenue distribution and dispute resolution. Academic associations and government departments should issue specialized copyright guidelines for the use of published data in AI training， regulate the compliant and efficient circulation of such data， explore the establishment of equitable revenue-sharing mechanisms， and guide the orderly development of the industry.

Purposes The purpose of this paper is to explore the effect of implementing open research data policies on the enhancement of journal academic influence from a quantitative perspective， thereby promoting the continuous improvement of journal academic impact and providing a theoretical basis and decision-making reference for journals in formulating related open research data policies. Methods The study focuses on Chinese journals indexed in the Chinese Science Citation Database （CSCD）. It first investigates the implementation status of open research data policies in these journals. Then， the difference-in-differences （DID） method is used to assess the policy effects of open research data on the academic influence of journals. Various robustness tests and heterogeneity analyses are conducted to further validate the reliability of the results. Findings The implementation of open research data policies significantly enhances the academic influence of journals， with policy effects showing significant heterogeneity across journals with different attributes. Conclusions By formulating and implementing open research data policies， journals can not only improve the transparency and reliability of research papers but also enhance their academic reputation， thereby expanding their academic influence and societal impact.

Purposes By reviewing the current research status of academic journal evaluation in China from 2014 to 2025， this paper explores the focus content， development status， and existing problems of academic journal evaluation research in China. Based on this， scientific and reasonable development suggestions are proposed to promote the smooth development of academic journal evaluation work in China. Methods The Citespace quantitative analysis tool was mainly used to conduct bibliometric analysis on the research papers on journal evaluation published in CSSCI core journals from 2014 to 2025， and to explore the current status of journal evaluation research. Findings The study found that research on journal evaluation in China from 2014 to 2025 has entered a period of rapid development， with authors and institutions exhibiting clustered development characteristics. Keywords such as “journal evaluation” “academic journals” and “impact factors” have become the focus of research. The core content of the research mainly focuses on the study of journal evaluation methods， evaluation index research， evaluation system construction， and rational reflection on journal evaluation work. Conclusions In order to better promote the evaluation of academic journals in China， on the one hand， it is necessary to continuously improve the multi-dimensional comprehensive evaluation system and construct a flexible evaluation mechanism that integrates layers and disciplines； On the other hand， it is necessary to establish a scientific evaluation system that is in line with Chinese characteristics and recognized by the international community， actively promote the implementation of research results in journal evaluation in China， and contribute to the high-quality development of academic journals.

Purposes The dissemination of academic short videos by scientific journals is an important means of integrated communication for scientific journals. Master's and doctoral students are among the main groups watching academic short videos. By investigating and analyzing the motivations behind the viewing behaviors of maste’s and doctoral students towards academic short videos from scientific and technological journals， we can identify the operational issues of these academic short videos， thereby providing references for enhancing their content production and operation. Methods A questionnaire survey was conducted among master’s and doctoral students. A research model was constructed based on the expectation-confirmation theory to understand the viewing behavior and motivation of this group， and operational strategies for academic short videos of scientific journals were proposed. Findings The study found that the continuous viewing intention of master’s and doctoral students stems from content satisfaction and the level of expectation confirmation. The information value of academic short videos can affect users’ perceived usefulness and satisfaction， while expectation confirmation can drive users to develop viewing interest and positive attitudes. The information value of academic short videos indirectly and positively influences the continuous viewing intention of master’s and doctoral students through perceived usefulness. Conclusions Scientific journals should strengthen the integration of resources on academic short video platforms. The operation of academic short videos needs to focus on disciplinary hotspots to enhance information value， explore one-click traceability to improve the level of knowledge services， and explore the profitability of academic short videos based on user needs.

Purposes To address the limitations of existing reviewer recommendation methods in semantic matching accuracy and explainability， this paper designs and implements an intelligent reviewer recommendation system （IRRS） integrating a large language model （LLM） with retrieval-augmented generation （RAG）. Methods Using papers published in 2025 in Intelligent Computing as the evaluation dataset， this study constructs an external knowledge base derived from Web of Science literature in computer science and related interdisciplinary fields and develops a dual-layer prompt framework to guide a large language model in generating candidate reviewers with high thematic alignment to the target manuscript. Findings The case-based comparison and evaluator assessments indicate that the proposed system outperforms the Scopus recommendation results in terms of thematic alignment and demonstrates strong explainability in its recommendation rationales. Conclusions The proposed LLM-RAG-based reviewer recommendation framework improves semantic matching and enhances the explainability of recommendation results. By integrating an external knowledge base with a dual-layer prompt design， the framework enables a flexible and scalable recommendation process without requiring modifications to the underlying model architecture. These findings suggest a practical pathway for incorporating large language models into scientific journal peer-review workflows.