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 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 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 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 Aiming at the problem of the spread of incorrect knowledge caused by the lag in retracting academic papers， this study aims to shorten the identification cycle of abnormal paper status and maintain the reliability and integrity of the academic communication system through AI technique. Methods Integrate the metadata and peer reviews contained in 12，098 papers from authoritative platforms such as PubPeer and PubMed as data support， develop a hybrid enhanced detection framework for paper retraction risk， and make decisions through retrieval-enhanced generation and expert-enhanced generation results. Findings This framework can accurately and effectively identify retracted papers. The verification accuracy rate of the retracted paper status reaches 91.91%， and the recall rate reaches 73.72%. Conclusions This study confirms the technical feasibility of large language models in academic early warning research and academic integrity maintenance. The application of the hybrid enhanced detection framework can provide a practical retraction early warning plan for publishing institutions. The open sharing of the hybrid enhanced detection framework will promote the standardization process of research on using large language models to handle retraction risk detection， help build an active monitoring scientific research integrity management system， improve supervision efficiency， and maintain the healthy development of the scientific research ecosystem.

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 To clarify the copyright risks triggered by text and data mining （TDM） and large language models （LLM） technologies in the commercial reuse of full-text big data in scientific journals， and to propose actionable compliance solutions based on this analysis. Methods A literature review and case analysis were conducted， issueed by focus group interviews， to extensively collect issues and cases concerning the reuse of scientific journal copyrights in the era of big data. Differences between China’s Copyright Law and the copyright legislation of major global economies （the United States， Japan， the European Union， and the United Kingdom） regarding TDM and LLM usage were examined. The study analyzes the copyright risks associated with the reuse of full-text big data in Chinese scientific journals and discuss and generate compliance authorization recommendations for journals to address potential risks. Findings In light of domestic scientific journal practices， five major copyright risks in the reuse of full-text big data are identified. Against the backdrop of open science， we recommend that journals and authors give priority to “copyright licensing （exclusive/non-exclusive）” over traditional “copyright assignment”clauses. When entering into licensing agreements with databases， journals must ensure they have legally obtained the necessary copyright authorization from authors and clearly define the scope of authorization in the agreement to prevent unauthorized sublicensing to databases beyond the granted rights. We recommend renegotiating copyright-licence agreements among authors， journals， and full-text databases， so that a clear and compliant authorization chain of “authors-journals-databases” should be established. At the same time， we recommend that the government take the lead in establishing a national-level green open storage platform similar to PubMed Central， to provide the necessary infrastructure for the compliant reuse of big data. Conclusions Journals should re-sign copyright agreements with authors and databases that comply with the reuse of full-text big data， improve the rules for journal data security and copyright usage， and encourage scholars to make their research findings more openly accessible. This study provides strategies for balancing copyright protection and data reuse in the era of big data for scientific journals， proposing a set of practical and feasible compliance measures. These measures ensure that journals fully utilize big data resources while respecting authors’ rights， promoting the open sharing and innovative application of academic information.

Purposes In response to the profound changes that the wave of open science （OS） is bringing to the research ecosystem， this paper aims to address the structural predicaments of “entrenched academic evaluation” and “weak industrial services” faced by industrial scientific journals， and to explore their sustainable transformation pathways. Methods Employing a “problem-function-path” analytical framework and integrating theories of open science， knowledge governance， and innovation ecosystems， this study conducts an in-depth analysis of the evolving roles and operational mechanisms of journals in knowledge coordination， innovation driving， and industrial linkage， supported by typical cases and data. Findings The study reveals that the essence of journal transformation is a functional leap from a traditional communication medium towards a “knowledge-governed innovation intermediary platform”. The core of this transformation lies in constructing three organization hub， an innovation empowerment engine， and a guided by the industrial linkage platform by the “function-dimension-adaptation” framework. This is ensured through five interconnected mechanisms： institutional guidance， capability upgrading， benefit realization， system reconstruction， and cyclical optimization. Furthermore， corresponding deepening measures are proposed. Conclusions The transformation of industrial scientific journals is a systematic project involving synergistic functional expansion， mechanism innovation， and strategic reshaping， which can provide a pivotal support point for building a new， open， collaborative， and sustainable ecosystem for industrial knowledge services.

Purposes In response to the problem in the layout design of scientific journals where the emphasis on form aesthetics is strong but the functional dissemination efficiency is insufficient， this study explores the theoretical basis and practical approaches for applying the Gestalt visual perception theory to the layout design of scientific and technological journals. Methods Starting from the functional positioning of efficient dissemination of scientific and technological journals and the visual perception cognitive mechanism of readers， this study demonstrates the applicability of the Gestalt theory， then systematically explains its core principles such as similarity， proximity and figure-background， and analyzes the specific application methods of these principles in the layout design of scientific and technological journals. Findings A set of layout design methods for scientific and technological journals aimed at improving information dissemination efficiency and reading experience has been constructed， providing theoretical support and operational strategies for the transformation of layout design from “aesthetic orientation” to “functional orientation”. Conclusions The Gestalt theory can provide a scientific cognitive psychology foundation for the layout design of scientific and technological journals. Its application can effectively construct a rational and clear visual order， which is an effective path for achieving efficient dissemination of scientific and technological information.

Purposes This paper aims to systematically review research progress on the concept， forms， and identification methods of citation manipulation in scientific publications. It critically summarizes the advantages and limitations of existing methods， identifies future research directions， and provides a theoretical basis for developing citation manipulation identification and prevention mechanisms suited to China's academic evaluation system. Methods A systematic literature review was conducted. Relevant literature was retrieved from the China National Knowledge Infrastructure （CNKI） and the Web of Science Core Collection databases. After screening， 57 highly relevant publications were included for analysis， focusing on dimensions such as conceptual definitions， manipulation forms， and identification methods. Findings Citation manipulation behaviors can be categorized into author-led （e.g.， excessive self-citation， reciprocal citation） and journal-led （e.g.， journal self-citation manipulation， citation cartels）. Identification methods are primarily classified into four categories： methods based on bibliometric indicators， citation network analysis， citation content analysis， and multi-dimensional fusion approaches. Each method type possesses distinct advantages and limitations regarding identification efficiency and applicable scenarios. Conclusions Current research on identifying citation manipulation faces challenges such as a lack of a unified conceptual framework and insufficient adaptability to emerging manipulation tactics. Future efforts should strengthen foundational theory， promote multi-dimensional feature fusion and interdisciplinary methodological innovation， develop dynamic detection technologies， and conduct localized research tailored to the Chinese academic ecosystem. Addressing citation manipulation requires collaboration among journals， authors， and research evaluation institutions to foster a research environment based on integrity and guided by diversified evaluation metrics.

Purposes To explore the bibliometric characteristics of retracted papers and their citing papers， assess the diffusion effects of citing retracted papers within citation chains， analyze whether academic misconduct can propagate through citation networks， and evaluate its potential impact on the scholarly community， providing insights for research output management and research integrity promotion. Methods Based on the retraction watch and openalex databases， over 50000 retracted papers and their citing papers were collected， resulting in a dataset of more than 600000 citing papers and over 1 million citation links. Using bibliometric methods， a systematic analysis was conducted from the perspectives of temporal evolution， team characteristics， national distribution， journal attributes， and citation features. Findings In recent years， the number of retracted papers has increased significantly， particularly in high-impact journals. The United States， China， and India are the main source countries of both retracted and citing papers. Moreover， papers that cite retracted works face a higher risk of retraction. Conclusions Retracted papers still exhibit sustained diffusion effects within academic networks， revealing gaps in citation norms and academic governance. It is recommended to establish real-time retraction labeling and citation warning mechanisms to curb the secondary dissemination of academic misconduct and maintain the robustness of the research integrity system.

Purposes To reveal the authors’ emotional perception and focus on academic journals， and provide empirical support for academic journals to improve the authors’ submission experience， enhance the author’s willingness， and optimize the brand and social reputation. Methods Using the SnowNLP emotion analysis model， we conducted sentiment analysis on 854 online comments regarding CSSCI source journals in a certain discipline from eshukan. Subsequently， we manually coded the positive and negative sentiment comment texts using Nvivo software. Findings The study found that the authors’ experience of submitting manuscripts in academic journals was dominated by negative emotions. The authors not only focused on the generally accepted peer review cycle and quality， but also paid high attention to the speed， quality and standardization of manuscripts processing by the editorial department， the collection of manuscripts fees， and the support of young talents. Conclusions Based on the grounded theory analysis of positive and negative comment text， it is proposed that in terms of manuscript processing， Chinese academic journals should pay attention to the timeliness of the notification of the results of manuscript use， and avoid the “management vacuum” state of the manuscript processing cycle in the review stage； In terms of supporting young talents， we should not simply evaluate articles based on professional titles， but actively establish columns for young scholars； In terms of fee collection， disclose various charging standards in advance and adjust the charging time point； In terms of establishing a good reputation， it is important to pay attention to communication with authors， handle special manuscripts carefully， avoid the rapid spread of bad reputation caused by improper handling； By constantly optimizing the author’s submission experience， accumulating the author’s positive emotional perceptions of the journal， and shaping a good brand image of the journal.

Purposes This paper aims to systematically review the development status of precise push for scientific journals globally， identify the key challenges faced by Chinese scientific journals in its practice， and forecast future development trends. The goal is to provide theoretical references and practical pathways for enhancing the dissemination capacity and influence of Chinese scientific journals. Methods Employing the literature research method and case analysis method， this study conducted a systematic retrieval of relevant literature from the CNKI （China National Knowledge Infrastructure） database spanning the years 2015 to 2025. Based on an in-depth analysis and synthesis of representative cases， an evolution framework from “editor-driven precision” to “algorithm-driven precision” was constructed. Findings The research finds that the precise push of Chinese scientific journals has gradually shifted from the “editor-driven precision” paradigm， reliant on editorial experience， towards the “algorithm-driven precision” paradigm centered on big data and intelligent algorithms. This transition has achieved significant improvements in push efficiency and service scale. However， challenges such as vague user profiles， data silos， algorithmic information narrowing， and insufficient content production collaboration persist. Conclusions Future development of precise push for Chinese scientific journals should advance towards intelligent and efficient dissemination， integration into scientific research workflows， user-participatory interaction， and scenario-intelligent integration. While leveraging technological empowerment， it is crucial to strengthen the deep integration of data ethics governance and a new academic evaluation system to achieve sustainable and high-quality academic communication.

Purposes To precisely grasp the strategic transformation path of Chinese scientific journals in a period of profound transformation in the research ecosystem， this study focuses on the leading journals of the phase II leading journals of the “Excellence Action Plan for China’s STM Journals”. It aims to systematically analyze the current development status of their academic communities and to provide references for future optimization. Methods This study employs a combination of web-based research， literature review， and comparative analysis. It not only conducts an in-depth investigation into the innovative practices of leading journals， such as providing research and science popularization services， constructing multi-channel communication systems， and empowering scholars’ career development， but also clearly defines the unique value and inherent limitations of journal-based academic communities through a horizontal comparison with virtual academic communities. Findings The findings reveal that leading journals in China have successfully broken through the singular function of traditional publishing， preliminarily establishing a multifunctional academic ecosystem that integrates research services， science communication， promotion of industry-academia-research integration， and talent support. However， compared to virtual communities， they still exhibit deficiencies in interaction depth， openness and transparency， and technological empowerment. Conclusions The future development of journal-based academic communities should be driven by an engine of “technological empowerment + multi-dimensional services”. By innovating interaction mechanisms and deepening technological integration， journals can form a collaborative and mutually beneficial partnership with researchers.

Purposes Conduct a comprehensive scan of the international preprint development landscape to provide a scientific basis for science and technology development decision-making， aiming to promote the development of Chinese preprints and enhance their influence. Methods Based on the preprint database integrated on the WoS platform， preprints published on major platforms such as arXiv， bioRxiv， medRxiv， ChemRxiv， and Preprints were used as data sources. The disciplinary layout and institutional distribution were analyzed， and academic and social influence indicator models were constructed to analyze the development trend and influence of preprints both domestically and internationally. Findings In 2024， the total number of preprints published on major international platforms was 344 thousand， mainly concentrated in fields such as computer science， artificial intelligence， mathematics， and computer science software engineering， which received significant academic citations and social impact. According to statistics from China as the first author， the total number of preprints in China in 2024 is 50 thousand， an increase of 43.7% compared to 2023， mainly concentrated in fields such as computer science， artificial intelligence， software engineering， and mathematics. Conclusions Preprints have become an effective channel for accelerating scientific exchanges， and the scale and academic influence of preprints in China have grown rapidly. Over the past decade， the scale and social dissemination of international preprints have increased significantly. Although the average number of academic citations per Chinese preprint is not much different from that of the United States， China still lags behind some European and American countries in terms of social dissemination influence， including news coverage and mentions on social media platforms.

Purposes To explore the situation of data reuse papers in Chinese scientific journals and its relationship with the journal impact， and to provide references for improving the influence of Chinese scientific journals. Methods This study analyzed 5662 citable papers from 24 journals in the medical and health sciences to examine the prevalence of data reuse papers and their relationship with journal impact metrics， including the composite impact factor， 5-year composite impact factor， academic journal clout index， journal mass index， and total citations. Findings Data reuse papers accounted for 10.21% of publications in Chinese medical and health science journals， with significant variation across different journals. Data reuse papers have higher average citations and downloads per paper than non-data reuse papers. Core journals demonstrated a significantly higher proportion of data reuse papers， along with greater average citations and downloads per paper compared to non-core journals. Data reuse papers derived from government and organizational datasets and those from universities and research institutions were the most frequently published and showed the highest impact. The proportion of data reuse papers， average citations per paper， and average downloads per paper all showed significant positive correlations with the journal impact metrics. Conclusions Journals can enhance their academic influence by increasing the publication of data reuse papers. Academic journals should emphasize the dissemination of data reuse papers to further promote scientific data sharing and advance open science practices in scholarly publishing.

Purposes In the context of building world-class journals， this paper analyzes the characteristics and academic impact of the papers that designated as “editors’ suggestions”， providing positive reference for Chinese journal editors to more scientifically and effectively select high-level papers. Methods Descriptive statistics， non-parametric tests， and logistic regression methods are used to analyze the characteristics of authors， institutions， countries， research fields， titles， funds， and references of the selected editors’ suggestions papers in Physical Review Letters from 2012 to 2021， and to explore the influence of this kind of papers from three aspects： overall， same author-same year-same field， and becoming highly cited papers. Findings he papers selected as editors’ suggestions have characteristics such as a large number of collaborators， a wide range of cooperating institutions， a clustering of countries and institutions， fields differences， concise titles， multiple supporting funds， and a focus on balancing old and new references. Compared to non-selected papers， the selected papers perform better in terms of citation frequency， usage since 2013， and becoming highly cited papers. Conclusions Given the significant academic impact of papers selected as editors’ suggestion， when promoting this model in the future， more attention should be paid to papers in the fields of large-scale collaboration and small but refined research. Furthermore， the conciseness of titles， the rationality of funding annotations， and the balance of old and new references should be emphasized. In addition， the author’s identity label should be treated rationally， and the professional level of the editorial team should also be continuously improved.