ISSN：1001-7143 CN：11-2684/G3
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Purposes Maintain an open attitude towards the rapidly evolving generative artificial intelligence （GenAI）， apply it to multiple stages of English scientific journal publishing， preserve the integrity of academic literature， and improve the publication efficiency. Methods Seven AI tools were selected and tested in our routine publishing workflows （Jianziyuan， Grammarly， FigCheck， AJE， DeepEdit， ChunlinAI-Editor， and Doubao）. Their performance was evaluated in terms of machine-generated text detection， image similarity checking， English language polishing， English manuscript editing， Chinese abstract revision， and promotional material generation/refinement. Findings For machine-generated text detection， the test results showed a relatively high false-positive rate； for image similarity checking， the tested AI tool could effectively detect problematic images； for English language polishing， the tested AI tools showed effectiveness； for English manuscript editing， the tested AI tool demonstrated high efficiency in identifying English spelling/grammar issues， as well as specific formatting ones； for Chinese abstract revision， the tested AI tool showed key advantage in the accurate translation of domain-specific terms； for promotional material generation/refinement， the tested AI tool showed high efficiency and good user-friendliness. Conclusions AI tools can effectively empower academic publishing. Currently， all outputs generated by AI tools strongly undergo final manual review by editors. We adopt a human-centered， gradual advancement strategy： in areas where AI excels， fully leverage its capabilities； in areas where AI shows potential， we stay tuned and proceed with cautious verification.
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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.
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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.
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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.
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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.
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Purposes To comprehensively analyze the strategic implications and practical obstacles of the scientific journal system in local colleges and universities， and provide references and suggestions for the construction of this system. Methods By means of literature research and case investigation， along with methodologies including inductive generalization and logical analysis， this paper demonstrates the value of constructing the scientific and technological journal system for local universities， dissects the obstacles and their underlying causes in the construction process， and clarifies and optimizes the logical correlations among relevant strategies. Findings Targeting issues such as the hysteresis of policy follow-up， the superficiality of subject cognition， the lag of journal-running practices， the misalignment of evaluation standards， and the absence of operational guarantees， optimization strategies are proposed from the aspects of strengthening policy coordination， reconstructing top-level design， stimulating endogenous motivation， iterating the evaluation system， and improving full-chain governance. Conclusions The scientific journal system in local colleges and universities has strong practical demand and strategic significance. It is necessary to identify and eliminate obstacles， and adopt appropriate strategies for steady construction.
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Purposes This article summarized the high-quality development model of industrial service-oriented scientific journals， with the aim of providing theoretical and practical guidance for China’s scientific journals. Methods Define the concept of industrial service-oriented scientific journals. Taking China Tea as an example， distill the development path and experience inspirations of such journals. Findings The high-quality industrial service model and practice of industrial service-oriented scientific journals include four aspects： the reconstruction of editor roles， the reorganization of knowledge production， the establishment of service platforms， and the creation of brand networks. Ultimately， it aims to simultaneously achieve high-quality journal construction and industrial services. Conclusions Industrial service-oriented scientific journals provide support and guidance through means such as upgrading the quality of journals， widely disseminating disciplinary research， innovating the forms of journal achievement output， and forming brand-oriented industrial clusters. This not only enhances the content influence and industrial influence of the journals but also optimizes their income structure and strengthens their self-sustaining capacity. And contribute to the high-quality development of the industry where it is located.
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Purposes This study examines the characteristics and drivers of preprint withdrawals to inform governance strategies for preprint platforms and their collaborative development with academic journals. Methods Leveraging a dataset of 19767 retracted preprints from arXiv （from 1991 to 2024）， we employed quantitative and textual analysis to examine withdrawal characteristics across temporal， disciplinary， and collaborative dimensions， and utilized zero-shot classification to automatically identify and categorize the reasons for retraction. Findings Our analysis revealed that solo-authored and small-team preprints were retracted more frequently， mostly within a year of posting， while larger teams demonstrated greater stability. Combinations of disciplines like Computer Science and Statistics showed a higher incidence of retraction. Furthermore， author-initiated retractions were predominantly for errors， in contrast to platform-initiated retractions， which largely concerned academic misconduct. The reasons for author-initiated retractions are closely associated with paper characteristics： papers with multiple authors and interdisciplinary backgrounds are more likely to be retracted due to author disputes， while single-discipline， low-version papers are primarily retracted due to errors； platform-initiated retractions show no such association. Conclusions Preprint retractions are influenced by collaboration scale， disciplinary field， and the initiating entity. This study provides empirical support for building early-warning mechanisms on preprint platforms and optimizing the peer-review process for journals， offering insights for constructing a research integrity framework within the open science ecosystem.
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Purposes To address the common challenges of resource limitations and declining dissemination in scientific journal websites， this study proposes the “citation showcase” model， exploring its theoretical rationale and practical potential. It aims to develop a feasible solution for activating journals’ implicit information resources and reconstructing information dissemination pathways， thereby overcoming resource bottlenecks and systematically enhancing journal dissemination effectiveness. Methods A functional prototype centered on the subjective display of citing literature， incorporating a three-tiered pathway of “traffic diversion → association → conversion”， was designed and deployed on the official websites of scientific journals for case studies. Evaluation was conducted through comprehensive analysis of traffic data and user behavior. Findings Practical application demonstrates that the “citation showcase” effectively expands the content boundaries and traffic entry points of journal websites. Key metrics such as user visits， dwell time， and content click-through rates on related pages improved， preliminarily validating its mechanisms in three dimensions： broadening entry points， constructing connections， and internalizing value. Conclusions The “citation showcase” offers a novel approach worthy of exploration for the digital transformation of journals. It revitalizes the implicit static resources of journal websites into dynamic dissemination platforms and can be extended to broader application conversions， providing a concrete and feasible solution for scientific journals to overcome content limitations and activate academic network influence in the digital era.
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Purposes To analyze the current state of data publication policies for articles in Chinese economics and management journals， enhance theoretical understanding of scientific data and data publishing within the social sciences， and promote high-quality open access and sharing of data. Methods This study employs a comprehensive approach integrating online surveys， text analysis， in-person interviews， and statistical analysis. Using the Top 50 academic journals in economics and management ranked by composite impact factor on China National Knowledge Infrastructure （CNKI） as research samples， it systematically organizes and summarizes key elements of their data publication policies. Findings Among 50 high-impact management journals sampled， only 15 （30%） have data publication policies. These policies were introduced relatively late， exhibit weak enforcement， feature limited data formats， and rely on fragmented publication channels. Statistical analysis of data disclosure across all articles published in Journal of Quantitative & Technological Economics from 2023 to 2024 indicates notably effective implementation of data publication practices. Conclusions Chinese economics and management journals face four major challenges in data publishing： academic ecosystem， infrastructure， human resources， and legal safeguards. To advance economics and management research toward a more open and transparent phase， improvements should focus on strengthening top-level design and incentive mechanisms， standardizing data formats and infrastructure， enhancing data management capabilities and talent reserves， and clarifying rights， responsibilities， and legal boundaries.
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Purposes To investigate the current situation of the digital and intelligent literacy among academic journal editors in the context of new quality productivity and construct cultivation pathways to empower the high-quality development of academic journals. Methods Based on preliminary research， an online questionnaire was developed to assess the editors’ digital-intelligent literacy. We distributed online questionnaires via We Chat and QQ groups to 348 editors using WJX （an online platform for questionnaire design and data collection）. Empirical analysis was conducted based on the data from the questionnaire survey. Findings The survey revealed that digital and intelligent tools are commonly used by academic journal editors （68.1%）. Editors demonstrated proficiency in basic skills such as academic database utilization （91.4%）， social media promotion （62.1%）， and AI-assisted peer review （85.3%）. However， the applications in fields such as data visualization analysis， digital layout， and data mining of journal impact were relatively insufficient. Significant differences were found in digital editing competency scores among editors with varying educational backgrounds， professional titles， and familiarity with digital tools （P<0.01）. Most editors identified the lack of systematic training as the primary obstacle to applying digital and intelligent tools （77.6%）， with training demands highly concentrated on practical skills like AI-assisted peer review （85.3%）， and data analysis and visualization （76.7%）. Conclusions Based on the empirical analysis， the study proposes three basic principles： internal-external collaboration， integration of theory and practice， and precise identification. It constructs a framework for cultivating digital and intelligent literacy among academic journal editors， involving a collaboration among individual editors， editorial offices， and industry associations. This framework covers the overall process of internal motivation activation， learning planning， institutional safeguards， practical empowerment， standard guidance， and platform support.
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Purposes This study aimed to explore the reshaping of professional values and competency transformation of scientific journal editors in open science， and propose career development paths adapted to the new environment. Methods Document analysis， model-based analysis， and the inductive method were used to investigate changes in academic publishing processes and their impact on the career development of editors in the context of open science. The study summarizes new requirements that open science places on editors’ professional competence at both the cognitive and practical levels， and proposes individual development paths accordingly. Findings To better adapt to the changes introduced by open science， editors should develop a deeper understanding of open science and redefine their professional roles from a cognitive perspective. From a practical standpoint， they should cultivate essential professional skills based on shifts in their priorities and duties. Conclusions Open science represents an inevitable trend that aligns with the evolution of science and the dissemination of knowledge. Editors of scientific journals should actively embrace open science and position themselves as leading advocates and participants of this trend. Specifically， they are advised to contribute to developing novel academic communication models that foster professional value creation. It is equally essential to build layered market-oriented skills that suit both the current environment and personal competencies. Further， editors should aim to become multi-skilled talents capable of handling diverse professional duties， thereby enhancing their professional self-efficacy and realizing their career development within the open science era.
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Purposes It explains the role of each stage of the academic journal manuscript processing in cultivating the academic abilities of authors， providing a reference for academic journals to facilitate the growth of authors. Methods From the perspective of editorial practice， an academic ability framework consisting of five dimensions-problem discovery， scheme design， paper writing， academic communication， and academic ethics-was constructed. By combining theoretical analysis with case evidence， the intrinsic association mechanism between the entire process of manuscript processing and academic ability cultivation was analyzed. Findings The initial review stage can enhance the author’s ability in writing papers and their awareness of academic ethical norms； the external review stage， through experts’ quality control of the content， improves the author’s ability to identify problems and design research plans； the revision stage， through two-way interaction， sharpens the author’s academic communication and paper-writing skills； the final review and appeal stages can cultivate the author’s academic perspective and the ability to debate based on evidence； the editing and proofreading stage can promote the author to internalize academic rigor in their work. Conclusions While maintaining the core focus on academic quality， the standardized and structured manuscript processing system of scientific journals can help cultivate the academic abilities of authors.
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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.
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Purposes To explore the mechanism of scientific journals in the paper-policy co-construction， so as to enhance their capacity for innovation leadership and knowledge translation. Methods Taking the policy texts， academic papers and column introductions of the new-generation information technology industry from 2015 to 2025 as data sources， 45 authoritative Chinese scientific journals were selected. BERTopic modeling was adopted， and semantic similarity analysis and time-series analysis were applied to distinguish three types of co-construction relationships. The mediating mechanism of journals was verified by combining quantitative indicators with qualitative text analysis， and the mechanism of action and applicable boundaries were elaborated through multi-case comparison. Findings The paper-policy nexus presents three types of co-construction relationships， namely policy transmission， academic diffusion and two-way circulation. As a mediating hub， scientific journals possess the function of two-way transformation； journals participate in the co-construction process through three mechanisms： strategic issue translation， frontier consensus incubation and collaborative knowledge transformation. Conclusions Scientific journals should transcend the role of traditional carriers， and become a strategic bridge connecting knowledge production and application by establishing a context-driven dynamic adaptation mechanism， restructuring the full-chain knowledge translation process， and improving the multi-factor collaborative support system of “talent-institution-technology”.
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Purposes Based on the core dimension of “providing revision support for authors”， and combined with the IMRaD structure， this study classifies Chinese peer review comments， explores the impact of their quantitative and content characteristics on article citation frequency， and aims to provide references for journals to optimize review processes and for authors to improve manuscript quality. Methods Taking 65509 sentence-level peer review comments from Acta Psychologica Sinica （from 2014 to 2022） as the research sample， the comments were categorized into five types： theoretical， research design， results-related， writing-related， and other. A negative binomial regression model was constructed with review rounds， number of review comments， length of review comments， whether editorial board members provided revision comments， and review comment types as independent variables； citation frequency as the dependent variable； and article age， number of authors， presence of funding support， number of references， title length， number of pages， affiliated institutions of the article， and inter-institutional collaboration as control variables. Findings Review rounds， whether editorial board members provided revision comments， and theoretical comments had a significant positive impact on citation frequency， while results-related and writing-related comments exhibited a significant negative impact. The number and length of review comments have a weak effect on citation frequency. Conclusions It is suggested that scientific journals emphasize the importance of theory in their review guidelines， refine core review dimensions， set quantitative thresholds for substantive suggestions， and incorporate them into the review quality evaluation system. Authors should focus on the solidity of theoretical frameworks during manuscript writing， prioritize responding to theoretical comments during revisions， and address key issues. Scientific journals can collaborate with universities and research institutions to establish a high-quality case library of review comments， thereby enhancing manuscript quality and citation frequency.