时光飞逝,岁月如梭;蛇年渐去,马年将至.转瞬间,2025年已成尾声,2026新的一年正在向我们翩翩走来. 回顾2025年,我们坚守办刊宗旨,提升办刊能力.我们制定了《期刊质量与影响力建设提升方案》,剖析了影响因素,并明确了质量与影响力提升路径.同时,初步完成了"十五五"战略规划编制,其核心是研究和应用人工智能技术,全面审视和介入论文的评审、发布和评价过程.
[Purpose/Significance]Under the backdrop of the accelerated development of AI for Science(AI4S)and national strategies on"AI+Science and Technology",high-quality,computable corpus resources have become a critical factor for large-model pretraining and intelligent scientific discovery.As the social infrastructures,libraries are facing growing demands to transform toward the construction and provision of AI-oriented corpus resources and services.The article aims to clarify the annotation of the"AI corpus library"and to provide a reference framework for related theoretical research and practical implementation.[Method/Process]By employing conceptual and evo-lutionary analysis,the article clarifies the essential characteristics of the AI corpus library,interpreting it as a deep integration and functional reconstruction of"Database × Corpus × Digital Library".It analyzes functional require-ments from three dimensions:technological evolution,social application,and regulatory governance.Furthermore,by synthesizing the"text-as-data"model of HathiTrust and the"digital scholarship"practice of the British Library,the paper systematically summarizes the evolutionary logic of the transition from digital libraries to AI corpus li-braries.[Result/Conclusion]The study shows that the AI corpus library is a new type of knowledge infrastructure oriented toward"human-machine agent"entities,with multi-modal and computable corpora as its core organiza-tional objects.Its construction should follow an architectural system characterized by data-driven top-level logic,knowledge organization as the intermediate mechanism,and agent-based applications as the functional innovation.The corpusization of existing library collections,the establishment of"non-consumptive use"governance mecha-nisms,and the embedding of corpus services into digital scholarship workflows represent effective paths for librar-ies to achieve functional expansion and intelligent upgrading.
[Purpose/Significance]Major local science and technology projects generally reflect the will and strategic needs of the government,aiming to solve the"bottleneck"problems in key technical fields.These projects are characterized by their importance,scale,and specialization.Traditional methods of expert evaluation and statistical analysis can no longer meet the new requirements of the current artificial intelligence and big data.The comprehensive application of various evaluation methods,such as intelligence analysis and peer review,to conduct performance eval-uation can provide strong support for the planning management,and decision-making of local science and technology management departments.[Method/Process]This paper focuses on the major science and technology projects in Fu-jian Province to establish a performance evaluation index system based on six dimensions:project decision-making,implementation management,goal achievement,technical level,transformation effect,and sustainable impact.Next,it introduces a scientific and technological intelligence method to integrate intelligence analysis with scientific evalua-tions by supplementing the index system with technical intelligence-related indicators and adding intelligence analysis and evaluation to the evaluation process.Then,a comprehensive evaluation model based on the AHP and entropy weight cloud model is established.Finally,a case study of a specific project is used to verify the application value of the above method in the performance evaluation process of major local science and technology projects in Fujian Prov-ince.[Result/Conclusion]The research results demonstrate that the intelligence analysis and evaluation method re-duces the influence of expert subjectivity,making the evaluation results more accurate and objective.The AHP-entropy weigh-cloud model accounts for the fuzziness and uncertainty of the evaluation information,which helps to accurately evaluate the performance levels,quickly determine the key influencing factors,and further enhance the authenticity and effectiveness of evaluation results.So,the method proposed in this paper is applicable to the post-performance evaluation of major local science and technology projects.It can provide theoretical support and practical guidance for scientific and technological information agencies or other evaluation institutions.
[Purpose/Significance]Academic papers are the main form of disseminating and validating scientific knowledge.Integrating such knowledge into policymaking can improve the scientificity and rationality of documents.By evaluating the policy impact,it can effectively demonstrate how academic papers can be transformed into policy and explore a new perspective on the evaluation of academic paper impact.[Method/Process]This paper adopts text mining methods,such as Sentence-BERT,SBERTopic to construct an indicator system for evaluating policy impact of academic papers based on semantic association from three dimensions:mention-level,thematic potential,and sentence-level im-pact.An empirical analysis is conducted on academic papers and policy texts in artificial intelligence from 2010 to 2024.The entropy weight-TOPSIS method is applied to weight the indicators for a composite policy impact score.[Result/Conclusion]Only a few papers reveal high policy impact,with their topic distribution and content exhibiting a clear ten-dency.Under different evaluation criteria,there are significant differences in the influence of papers in the field of artificial intelligence.In the future,scholars and policymakers should collaborate to address the challenges in translating academic research into policy,establish and improve the platform of government think tanks,promote more and faster academic papers into the policy-making process,and explore a more comprehensive and effective evaluation system.
[Purpose/Significance]This study investigates the adaptability and evolutionary path of agent technologies in science and technology(S&T)intelligence work,aiming to meet the increasing demands for intel-ligent and collaborative systems.It introduces the concept of the Documentation and Information Service Agent(DIS Agent)and clarifies its theoretical foundation and system mechanisms,providing methodological support for advancing S&T intelligence systems toward higher-level cognitive capabilities.[Method/Process]Drawing on the stratification of agent capabilities and multi-agent collaboration models,the study examines their development in scientific and intelligence contexts.It proposes a three-stage system evolution path-from Management Infor-mation Systems(MIS),through AI Agent systems,to the Agentic AI paradigm-within the three major domains of information management,intelligence analysis,and scholarly communication.A task-closed-loop architecture is proposed to support the emergence of human-multi-agent collaboration mechanisms.[Result/Conclusion]DIS Agents are task-oriented systems that integrate multiple types of agents with large language models as cognitive cores,characterized by modular design and collaborative evolution capabilities.Under this architecture,the three core domains of intelligence work undergo a shift from rule-based configuration to semantic-driven systems,en-abling task-oriented knowledge organization,autonomous strategy generation,and optimized communication loops.The role of human experts evolves from operators to"intent architect for DIS task framework"and"process quality supervisors,"marking the formation of a new paradigm of human-multi-agent collaboration.Future work will focus on the development and practical deployment of DIS Agent systems.
