Journal of Informetrics最新文献

Unfinished grants, unending progress: The impact of unfinished research grants on scientific innovation 未完成的资助，永无止境的进步：未完成的研究资助对科学创新的影响

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-10-08 DOI: 10.1016/j.joi.2025.101734

Jiangyang Fu , Xin Liu , Chenwei Zhang , Jiang Li

{"title":"Unfinished grants, unending progress: The impact of unfinished research grants on scientific innovation","authors":"Jiangyang Fu , Xin Liu , Chenwei Zhang , Jiang Li","doi":"10.1016/j.joi.2025.101734","DOIUrl":"10.1016/j.joi.2025.101734","url":null,"abstract":"<div><div>Scientists may not fulfill the objectives delineated within their research proposals subsequent to the receipt of funding. The extent to which unfinished grants enhance scientific knowledge remains an open question. Drawing upon a dataset from the Research Grants Council of Hong Kong (RGC) that encompasses the years 2010 to 2020, and is distinguished by its inclusion of self-reported grant completion rates, this study seeks to assess the potential contributions of research grants that were not fully completed to the progress of scientific knowledge. The analysis is conducted by leveraging the RGC's detailed records of project completion rates. The results indicate that, notwithstanding a relative lack in productivity and impact, there is no evidence that unfinished grants generate knowledge that is less disruptive than that produced by completed grants. Consequently, it is suggested that funding bodies should consider revising their assessment criteria to recognize the intrinsic merit of grants that are traditionally labeled as unfinished, thus providing more flexibility for the exploration of novel research domains within the grant allocation process.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101734"},"PeriodicalIF":3.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multidimensional bibliometric assessment of science funding effectiveness 科学资助有效性的多维文献计量评估

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-30 DOI: 10.1016/j.joi.2025.101731

Tian-Yuan Huang , Wenjing Xiong

{"title":"Multidimensional bibliometric assessment of science funding effectiveness","authors":"Tian-Yuan Huang , Wenjing Xiong","doi":"10.1016/j.joi.2025.101731","DOIUrl":"10.1016/j.joi.2025.101731","url":null,"abstract":"<div><div>Science funding supports discovery, innovation, and societal progress by enabling research aligned with social needs, but its effectiveness is hard to assess due to the lack of counterfactuals, overlapping funding sources, and varied evaluation metrics. To address this, we developed a multidimensional framework that encompasses research impact, international collaboration, open access status, thematic orientation, and interdisciplinarity, and used it to compare publications funded by the U.S. National Science Foundation (NSF) and the National Natural Science Foundation of China (NSFC) across fifteen natural science fields between 2011 and 2020. Our analysis shows that NSFC support more effectively captures highly cited outputs, whereas NSF funding is more efficient at identifying high impact work and more consistently promotes international partnerships. Both funders have driven substantial growth in open access publishing even though the rising article processing charges threaten equity. In terms of thematic focus, NSFC concentrates on popular research areas while NSF tends to support niche but influential fields. Finally, funded publications consistently demonstrate superior interdisciplinary integration compared to unfunded publications before 2018, indicative of a systemic inclination within financially backed research endeavors to synthesize heterogeneous academic domains for enhanced innovative output. Funded publications outperform unfunded publications both on dimensions of variety and disparity, yet reverses on balance. These findings demonstrate that national funding schemes exert heterogeneous effects on research dynamics, suggesting that future policy should mandate incentives for collaboration and open access, diversify thematic portfolios, and prioritize genuine interdisciplinary innovation.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101731"},"PeriodicalIF":3.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated generation of research workflows from academic papers: a full-text mining framework 从学术论文中自动生成研究工作流：一个全文挖掘框架

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-22 DOI: 10.1016/j.joi.2025.101732

Heng Zhang , Chengzhi Zhang

{"title":"Automated generation of research workflows from academic papers: a full-text mining framework","authors":"Heng Zhang , Chengzhi Zhang","doi":"10.1016/j.joi.2025.101732","DOIUrl":"10.1016/j.joi.2025.101732","url":null,"abstract":"<div><div>The automated generation of research workflows is essential for improving the reproducibility of research and accelerating the paradigm of “AI for Science”. However, existing methods typically extract merely fragmented procedural components and thus fail to capture complete research workflows. To address this gap, we propose an end-to-end framework that generates comprehensive, structured research workflows by mining full-text academic papers. As a case study in the Natural Language Processing (NLP) domain, our paragraph-centric approach first employs Positive-Unlabeled (PU) Learning with SciBERT to identify workflow-descriptive paragraphs, achieving an F<sub>1</sub>-score of 0.9772. Subsequently, we utilize Flan-T5 with prompt learning to generate workflow phrases from these paragraphs, yielding ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.4543, 0.2877, and 0.4427, respectively. These phrases are then systematically categorized into data preparation, data processing, and data analysis stages using ChatGPT with few-shot learning, achieving a classification precision of 0.958. By mapping categorized phrases to their document locations in the documents, we finally generate readable visual flowcharts of the entire research workflows. This approach facilitates the analysis of workflows derived from an NLP corpus and reveals key methodological shifts over the past two decades, including the increasing emphasis on data analysis and the transition from feature engineering to ablation studies. Our work offers a validated technical framework for automated workflow generation, along with a novel, process-oriented perspective for the empirical investigation of evolving scientific paradigms. Source code and data are available at: h ttps://github.co<em>m/Z</em>H-heng/research_workflow.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101732"},"PeriodicalIF":3.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of rhetorical devices on citation behavior: persuasion in scientific papers and its effect on reader response 修辞手段对引文行为的影响：科学论文中的说服及其对读者反应的影响

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-18 DOI: 10.1016/j.joi.2025.101729

Kai Meng , Chungwon Koh , Zhejun Zheng , Zhichao Ba , Min Song

{"title":"Impact of rhetorical devices on citation behavior: persuasion in scientific papers and its effect on reader response","authors":"Kai Meng , Chungwon Koh , Zhejun Zheng , Zhichao Ba , Min Song","doi":"10.1016/j.joi.2025.101729","DOIUrl":"10.1016/j.joi.2025.101729","url":null,"abstract":"<div><div>Storytelling is an effective method for communicating science, with rhetorical writing offering a structured framework to enrich narratives and enhance their persuasive impact. Scientific writing does not inherently prioritize obscure or convoluted language; however, overly dry and impersonal scientific texts may be difficult to understand and engage with. This study investigates how rhetorical styles in scientific writing influence citation behaviors among readers. Building upon Aristotle's rhetorical theory, we construct computational measures for three key rhetorical strategies in scientific writing: authority (ethos), readability (logos), and emotions (pathos). Using a dataset of over 10 million journal articles from OpenAlex, we analyze the causal relationship between rhetorical styles in scientific writing and their impact on citation behaviors. Our findings reveal that (1) increased use of ethos and pathos in scientific writing positively influences citation counts, while logos has a negative causal effect; (2) author reputation significantly moderates the persuasive effects of rhetoric, particularly mitigating the negative impact of logos; and (3) rhetorical heterogeneity is influenced by factors such as country of publication, publishing formats, disciplines, and citation percentiles. These results offer valuable insights for early-career researchers on effective scientific writing and serve as a reference for publishers developing guidelines.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101729"},"PeriodicalIF":3.5,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring interdisciplinary research trends through critical years for interdisciplinary citation 通过跨学科引文的关键年份探索跨学科研究趋势

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-12 DOI: 10.1016/j.joi.2025.101726

Guoyang Rong , Ying Chen , Feicheng Ma , Thorsten Koch

引用次数: 0

Revealing the research differences of AI between China and the U.S using semantic deviation 利用语义偏差揭示中美人工智能研究差异

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-10 DOI: 10.1016/j.joi.2025.101728

Guo Chen , Han Sun , Xianzu Liu , Lu Xiao

{"title":"Revealing the research differences of AI between China and the U.S using semantic deviation","authors":"Guo Chen , Han Sun , Xianzu Liu , Lu Xiao","doi":"10.1016/j.joi.2025.101728","DOIUrl":"10.1016/j.joi.2025.101728","url":null,"abstract":"<div><div>China and the United States are recognized as leading forces in Artificial Intelligence (AI) research, with distinct research inclinations within their communities. Understanding the research differences between these two nations is crucial for grasping the global AI landscape, especially for revealing its collaborative division of labor and competitive situation. This paper moves beyond traditional methods reliant on frequency statistics and topic analysis by introducing an innovative approach that highlights the semantic deviation, which can help differentiate the details of research preference of a given research concept in two countries. We construct a matrix that includes two dimensions: research scale and semantic deviation, positioning each research concept into four areas including Discrepant Research, Interest-Vary Research, Consensus Research and Scale-Gap Research. Based on which, we conducted co-word network analysis to explore the research differences of China and U.S. on macro level, and utilized semantic field analysis to further explore its details in the case of “Face Recognition” at micro level. We found that in AI research between China and the U.S., the research scale difference is not significant for over 90 % of all domain entities, but 37.5 % of entities show a clear semantic deviation. The high-frequency entities that represent popular research issues also show the same results. Our findings indicate that AI researchers from both countries have a relatively consistent level of attention to the vast majority of domain concepts, yet there is still a significant difference in the content preferences between the two nations in terms of research being conducted. Our framework enables a thorough examination of research differences with various types, providing valuable insights into the distinctive research profiles and competition advantages in AI between China and U.S.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101728"},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Innovation lineage structure: A graph structure in publications of scholars and its association with disruptiveness 创新谱系结构：学者论文中的图表结构及其与破坏性的关系

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-10 DOI: 10.1016/j.joi.2025.101730

Xian Li , Haixing Du , Yi Bu , Mingshu Ai , Junjie Huang , Tao Jia

{"title":"Innovation lineage structure: A graph structure in publications of scholars and its association with disruptiveness","authors":"Xian Li , Haixing Du , Yi Bu , Mingshu Ai , Junjie Huang , Tao Jia","doi":"10.1016/j.joi.2025.101730","DOIUrl":"10.1016/j.joi.2025.101730","url":null,"abstract":"<div><div>Numerous factors have been associated with disruptive research that dramatically drives scientific development. However, few studies have explored the issue from the perspective of the publication structures of scholars. To fill the gap, we identified a graph publication structure, termed innovation lineage structure, from 110,488,521 publications in the <em>OpenAlex</em> database authored by 1523,664 scholars who began their careers in 1980 or later. Using logistic regression models, we found that publications within these structures were more disruptive than those outside. This finding remained robust across different disruptiveness measures, scholars of various genders, and within the natural and engineering sciences. Informed by career stages and knowledge diversity, we observed that scholars adopted exploration research strategies for research within their innovation lineage structures, leading to more disruptive impacts. The proposed innovation lineage structures are associated with disruptiveness and offer insights for scholars seeking greater impact, highlighting that publications grounded in novel work and characterized by persistent innovation are more likely to be disruptive.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101730"},"PeriodicalIF":3.5,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The increasing dominance of repeated citations from collaborative research groups in science 科学领域合作研究小组的重复引用日益占主导地位

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-09-03 DOI: 10.1016/j.joi.2025.101723

Xifeng Gu, An Zeng

引用次数: 0

Artificial intelligence in scientific research: Challenges, opportunities and the imperative of a human-centric synergy 科学研究中的人工智能：挑战、机遇和以人为中心的协同作用的必要性

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-08-31 DOI: 10.1016/j.joi.2025.101727

Francesco Branda , Massimo Ciccozzi , Fabio Scarpa

{"title":"Artificial intelligence in scientific research: Challenges, opportunities and the imperative of a human-centric synergy","authors":"Francesco Branda , Massimo Ciccozzi , Fabio Scarpa","doi":"10.1016/j.joi.2025.101727","DOIUrl":"10.1016/j.joi.2025.101727","url":null,"abstract":"<div><div>This work offers a critical and evidence-based synthesis of the conceptual, methodological, and social implications of artificial intelligence (AI) in scientific research, significantly enriched by an informetric perspective. The analysis transcends descriptive overviews and simple cataloging of products, providing a deeper understanding of the opportunities AI presents, such as accelerated data analysis, hypothesis generation, and drug discovery. At the same time, crucial challenges that AI introduces are explored, including knowledge monocultures, algorithmic bias, reproducibility issues, and the impact on research integrity and evaluation. The original contribution of this paper lies in the integration of informetric analysis to quantify the influence of AI on the production and dissemination of scientific knowledge, highlighting both its potential as an analytical tool and the risk of bias in the academic record. The paper emphasizes the need for frameworks that harmonize technological capabilities with the irreplaceable ingenuity of human thought, promoting balanced collaboration between AI and researchers, where AI serves as a tool to increase productivity and human oversight ensures ethical rigor, critical evaluation, and creative exploration.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101727"},"PeriodicalIF":3.5,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring the potential of novel research proposals as signals of successful national R&D: A case study on energy and resource sector in South Korea 探索作为国家研发成功信号的新颖研究提案的潜力：以韩国能源和资源部门为例

IF 3.5 2区管理学

Journal of Informetrics Pub Date : 2025-08-29 DOI: 10.1016/j.joi.2025.101722

Jaemin Chung , Janghyeok Yoon , Jaewoong Choi

{"title":"Exploring the potential of novel research proposals as signals of successful national R&D: A case study on energy and resource sector in South Korea","authors":"Jaemin Chung , Janghyeok Yoon , Jaewoong Choi","doi":"10.1016/j.joi.2025.101722","DOIUrl":"10.1016/j.joi.2025.101722","url":null,"abstract":"<div><div>The success of national R&D projects plays a vital role in sustaining the long-term growth of the domestic techno-economic system and strengthening the innovation capacity of the national innovation system. While successful R&D projects are often characterized by ex-post (e.g., significant R&D performance) and ex-ante (e.g., novel research content) factors, their empirical relationship remains unclear. This study quantitatively examines whether the novelty of research proposals serves as a potential indicator of successful national R&D. Using a transformer-based language model and a local outlier factor, we measure the semantic novelty of research proposals by measuring their differentiation from existing paradigms. We conduct a statistical analysis to examine how the novelty of research proposals moderates the effects of R&D investment on R&D performance. A case study of 12,243 research proposals in South Korea’s energy and resource sector shows that the proposed novelty indicator exhibits a statistically significant association with both R&D investment and performance levels. We also show that novelty positively moderates the relationship between R&D investment and performance. The empirical results are expected to provide insights into understanding successful national R&D projects by revealing the relationships between novel research proposals, R&D investment, and performance in various contexts. The proposed approach and its systematic process are expected to guide experts in continuously monitoring national R&D trends and evaluating research proposals in the era of open innovation.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101722"},"PeriodicalIF":3.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0