Pub Date : 2024-10-19DOI: 10.1016/j.joi.2024.101597
Le Chang , Huiying Zhang , Chao Zhang
The choice of research strategy is patterned by the essential tension between tradition and innovation. Drawing on the leadership continuum theory, this paper proposes a theoretical framework discussing the continuum of research strategy referred to as conventional learning. We explore how knowledge creation is affected by conventional learning and collaboration diversity. Relevant hypotheses are tested using data from the Web of Science (WoS) database between 1988 and 2018. The results indicate both focused and expansive conventional learning have a positive relationship with knowledge productivity, while they have a U-shaped effect on knowledge creativity. Collaboration diversity positively moderates the relationship between focused and expansive conventional learning and knowledge productivity. Furthermore, although low-level collaboration diversity is optimal for knowledge creativity when the level of conventional learning is low, high-level collaboration diversity is more beneficial for knowledge creativity when the level of conventional learning is high, both for focused and expansive. Our study provides important implications for creative individuals.
传统与创新之间的基本矛盾决定了研究战略的选择。本文借鉴领导力连续体理论，提出了一个讨论研究战略连续体（即传统学习）的理论框架。我们探讨了知识创造如何受到传统学习与合作多样性的影响。我们利用 1988 年至 2018 年期间来自科学网（WoS）数据库的数据对相关假设进行了检验。结果表明，专注型和扩展型常规学习与知识生产率都有正相关关系，而它们对知识创造力的影响呈 U 型。合作多样性正向调节了集中式和扩展式常规学习与知识生产率之间的关系。此外，尽管当常规学习水平较低时，低水平的合作多样性对知识创造力是最佳的，但当常规学习水平较高时，高水平的合作多样性对知识创造力更有利，无论是集中式还是扩展式。我们的研究为具有创造力的个人提供了重要启示。
{"title":"Should we circumvent knowledge path dependency? The impact of conventional learning and collaboration diversity on knowledge creation","authors":"Le Chang , Huiying Zhang , Chao Zhang","doi":"10.1016/j.joi.2024.101597","DOIUrl":"10.1016/j.joi.2024.101597","url":null,"abstract":"<div><div>The choice of research strategy is patterned by the essential tension between tradition and innovation. Drawing on the leadership continuum theory, this paper proposes a theoretical framework discussing the continuum of research strategy referred to as conventional learning. We explore how knowledge creation is affected by conventional learning and collaboration diversity. Relevant hypotheses are tested using data from the Web of Science (WoS) database between 1988 and 2018. The results indicate both focused and expansive conventional learning have a positive relationship with knowledge productivity, while they have a U-shaped effect on knowledge creativity. Collaboration diversity positively moderates the relationship between focused and expansive conventional learning and knowledge productivity. Furthermore, although low-level collaboration diversity is optimal for knowledge creativity when the level of conventional learning is low, high-level collaboration diversity is more beneficial for knowledge creativity when the level of conventional learning is high, both for focused and expansive. Our study provides important implications for creative individuals.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535415","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}
Pub Date : 2024-10-16DOI: 10.1016/j.joi.2024.101596
Guoxiu He , Chenxi Lin , Jiayu Ren , Peichen Duan
The exponential increase in the annual volume of publications places a significant challenge in assessing the disruptive potential of technologies in new papers. Prior approaches to identifying disruptive technologies based on the accumulation of paper citations are characterized by their limited prospective and time-consuming nature. Moreover, the total citation count fails to capture the intricate network of citations associated with the focal papers. Consequently, we advocate for the utilization of the disruption index instead of depending on citation counts. Particularly, we devise a novel neural network, called Soft Prompt-aware Shared BERT (SPS-BERT), to predict the potential technological disruption index of immediately published papers. It incorporates separate soft prompts to enable BERT examining comparative details within a paper's abstract and its references. Additionally, a tailored attention mechanism is employed to intensify the semantic comparison. Based on the enhanced representation derived from BERT, we utilize a linear layer to estimate potential disruption index. Experimental results demonstrate that SPS-BERT outperforms existing state-of-the-art methods in predicting five-year disruption index across the DBLP and PubMed datasets. Additionally, we conduct an evaluation of our model to predict the ten-year disruption index and five-year citation increments, demonstrating its robustness and scalability. Notably, our model's predictions of disruptive technologies, based on papers published in 2022, align with the expert assessments released by MIT, highlighting its practical significance. The code is available at https://github.com/ECNU-Text-Computing/SPS-BERT.
{"title":"Predicting the emergence of disruptive technologies by comparing with references via soft prompt-aware shared BERT","authors":"Guoxiu He , Chenxi Lin , Jiayu Ren , Peichen Duan","doi":"10.1016/j.joi.2024.101596","DOIUrl":"10.1016/j.joi.2024.101596","url":null,"abstract":"<div><div>The exponential increase in the annual volume of publications places a significant challenge in assessing the disruptive potential of technologies in new papers. Prior approaches to identifying disruptive technologies based on the accumulation of paper citations are characterized by their limited prospective and time-consuming nature. Moreover, the total citation count fails to capture the intricate network of citations associated with the focal papers. Consequently, we advocate for the utilization of the disruption index instead of depending on citation counts. Particularly, we devise a novel neural network, called Soft Prompt-aware Shared BERT (<strong>SPS-BERT</strong>), to predict the potential technological disruption index of immediately published papers. It incorporates separate soft prompts to enable BERT examining comparative details within a paper's abstract and its references. Additionally, a tailored attention mechanism is employed to intensify the semantic comparison. Based on the enhanced representation derived from BERT, we utilize a linear layer to estimate potential disruption index. Experimental results demonstrate that SPS-BERT outperforms existing state-of-the-art methods in predicting five-year disruption index across the DBLP and PubMed datasets. Additionally, we conduct an evaluation of our model to predict the ten-year disruption index and five-year citation increments, demonstrating its robustness and scalability. Notably, our model's predictions of disruptive technologies, based on papers published in 2022, align with the expert assessments released by MIT, highlighting its practical significance. The code is available at <span><span>https://github.com/ECNU-Text-Computing/SPS-BERT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442191","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}
Pub Date : 2024-10-03DOI: 10.1016/j.joi.2024.101595
Marek Kwiek , Wojciech Roszka
In this research, the contributions of a highly productive minority of scientists to the national Polish research output over the past three decades (1992–2021) is explored. A large population of all internationally visible Polish scientists (N = 152,043) with their 587,558 articles is studied. In almost all previous research, the approaches to high research productivity are missing the time component. Cross-sectional studies were not complemented by longitudinal studies: Scientists comprising the classes of top performers have not been tracked over time. Three classes of top performers (the upper 1 %, 5 %, and 10 %) are examined, and a surprising temporal stability of productivity patterns is found. The 1/10 and 10/50 rules consistently apply across the three decades: The upper 1 % of scientists, on average, account for 10 % of the national output, and the upper 10 % account for almost 50 % of total output, with significant disciplinary variations. The Relative Presence Index (RPI) we constructed shows that men are overrepresented and women underrepresented in all top performers classes. Top performers are studied longitudinally through their detailed publishing histories, with micro-data coming from the raw Scopus dataset. Econometric models identify the three most important predictors that change the odds ratio estimates of membership in the top performance classes: gender, academic age, and research collaboration. The downward trend in fixed effects over successive six-year periods indicates increasing competition in Polish academia.
{"title":"Top research performance in Poland over three decades: A multidimensional micro-data approach","authors":"Marek Kwiek , Wojciech Roszka","doi":"10.1016/j.joi.2024.101595","DOIUrl":"10.1016/j.joi.2024.101595","url":null,"abstract":"<div><div>In this research, the contributions of a highly productive minority of scientists to the national Polish research output over the past three decades (1992–2021) is explored. A large population of all internationally visible Polish scientists (<em>N</em> = 152,043) with their 587,558 articles is studied. In almost all previous research, the approaches to high research productivity are missing the time component. Cross-sectional studies were not complemented by longitudinal studies: Scientists comprising the classes of top performers have not been tracked over time. Three classes of top performers (the upper 1 %, 5 %, and 10 %) are examined, and a surprising temporal stability of productivity patterns is found. The 1/10 and 10/50 rules consistently apply across the three decades: The upper 1 % of scientists, on average, account for 10 % of the national output, and the upper 10 % account for almost 50 % of total output, with significant disciplinary variations. The Relative Presence Index (RPI) we constructed shows that men are overrepresented and women underrepresented in all top performers classes. Top performers are studied longitudinally through their detailed publishing histories, with micro-data coming from the raw Scopus dataset. Econometric models identify the three most important predictors that change the odds ratio estimates of membership in the top performance classes: gender, academic age, and research collaboration. The downward trend in fixed effects over successive six-year periods indicates increasing competition in Polish academia.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-27DOI: 10.1016/j.joi.2024.101593
Zhaoping Yan , Kaiyu Fan
Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.
{"title":"A multi-entity reinforced main path analysis: Heterogeneous network embedding considering knowledge proximity","authors":"Zhaoping Yan , Kaiyu Fan","doi":"10.1016/j.joi.2024.101593","DOIUrl":"10.1016/j.joi.2024.101593","url":null,"abstract":"<div><div>Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327622","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}
Pub Date : 2024-09-27DOI: 10.1016/j.joi.2024.101592
Guiyan Ou , Kang Zhao , Renxian Zuo , Jiang Wu
Assessing the effectiveness of research funding is of significant value to policymakers. Previous studies have mainly concentrated on the academic impact of funded research, yet the exploration of how research funding affects the societal visibility of research has been significantly lacking. Thus, this study addresses this gap by examining papers published by Chinese scholars and compares the effects of funding on papers’ societal visibility (measured by Altmetric Attention Scores) with those for papers' academic impact (measured by citation counts). This study reveals several interesting findings: First, research supported by funding demonstrates a lower societal visibility, albeit a higher academic impact, compared to those without funding. Second, the societal visibility of research supported by small to moderate number of funding sources is still lower than those without research funding. In contrast, a paper's academic impact is higher if it has a higher number of research funding sources. Third, the effects of funding on papers’ academic impact and societal visibility differ by funding mechanisms—having industry funding significantly increases the societal visibility of research. These findings can aid research policymakers’ funding allocation decisions and inform better assessment of research outcomes.
{"title":"Effects of research funding on the academic impact and societal visibility of scientific research","authors":"Guiyan Ou , Kang Zhao , Renxian Zuo , Jiang Wu","doi":"10.1016/j.joi.2024.101592","DOIUrl":"10.1016/j.joi.2024.101592","url":null,"abstract":"<div><div>Assessing the effectiveness of research funding is of significant value to policymakers. Previous studies have mainly concentrated on the academic impact of funded research, yet the exploration of how research funding affects the societal visibility of research has been significantly lacking. Thus, this study addresses this gap by examining papers published by Chinese scholars and compares the effects of funding on papers’ societal visibility (measured by Altmetric Attention Scores) with those for papers' academic impact (measured by citation counts). This study reveals several interesting findings: First, research supported by funding demonstrates a lower societal visibility, albeit a higher academic impact, compared to those without funding. Second, the societal visibility of research supported by small to moderate number of funding sources is still lower than those without research funding. In contrast, a paper's academic impact is higher if it has a higher number of research funding sources. Third, the effects of funding on papers’ academic impact and societal visibility differ by funding mechanisms—having industry funding significantly increases the societal visibility of research. These findings can aid research policymakers’ funding allocation decisions and inform better assessment of research outcomes.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327484","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}
Pub Date : 2024-09-25DOI: 10.1016/j.joi.2024.101594
Alex J. Yang
Understanding the diversity of scientific knowledge is pivotal for elucidating trends in science and innovation. While interdisciplinarity and team diversity have been extensively studied, the temporal diversity of knowledge remains underexplored. This paper introduces a novel framework for assessing temporal diversity in scholarly research. Analyzing 31 million articles from the past seven decades, I revealed an increasing trend in temporal diversity, reflecting the cumulative nature of scientific knowledge. Additionally, I found that temporal diversity is negatively associated with citation impact but positively associated with disruption. These patterns are robust and consistent across different contexts. Moreover, the findings suggest that higher temporal diversity leading to greater disruption may be primarily due to the use of older references. However, the disadvantages of temporal diversity in terms of citation impact cannot be entirely explained by other factors. Collectively, this study elucidates the dynamics of temporal diversity and its implications for innovation, providing new frameworks in the science of science and evidence on how innovation is driven by the temporal diversity of knowledge.
{"title":"On the temporal diversity of knowledge in science","authors":"Alex J. Yang","doi":"10.1016/j.joi.2024.101594","DOIUrl":"10.1016/j.joi.2024.101594","url":null,"abstract":"<div><div>Understanding the diversity of scientific knowledge is pivotal for elucidating trends in science and innovation. While interdisciplinarity and team diversity have been extensively studied, the temporal diversity of knowledge remains underexplored. This paper introduces a novel framework for assessing temporal diversity in scholarly research. Analyzing 31 million articles from the past seven decades, I revealed an increasing trend in temporal diversity, reflecting the cumulative nature of scientific knowledge. Additionally, I found that temporal diversity is negatively associated with citation impact but positively associated with disruption. These patterns are robust and consistent across different contexts. Moreover, the findings suggest that higher temporal diversity leading to greater disruption may be primarily due to the use of older references. However, the disadvantages of temporal diversity in terms of citation impact cannot be entirely explained by other factors. Collectively, this study elucidates the dynamics of temporal diversity and its implications for innovation, providing new frameworks in the science of science and evidence on how innovation is driven by the temporal diversity of knowledge.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319662","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}
Pub Date : 2024-09-18DOI: 10.1016/j.joi.2024.101591
Jong Hee Jeon, Jason J. Jung
This paper proposes a method to evaluate academic impact that focuses on spatial context in which citations occur in sections of citing papers. Previous studies measured impact of papers using external factors such as journals, time, and authors. However, these methods overlooks context of citations, leading to problem of treating papers with same citation counts equivalently. To overcome this issue, we designed a citation network by reflecting on the spatial context in which cited papers are cited in the citing paper and measured their impact. Spatial context is defined by the specific section of the citing paper (Introduction, Method, Result, Discussion, Conclusion) where the citation appears. We collected 818 citing papers and 13,257 cited papers from 2013–2022 from Journal of Informetrics and constructed a context-reflected citation network. Further, we utilized CRITIC method and weighted PageRank algorithm for measuring section-specific weights and impact. Results obtained in this study suggest that the impact of cited papers varies significantly depending on the section context in which they appear. We use Kendall τ coefficient for analyzing correlation between “times cited” rankings and contextual PageRank. The Kendall τ coefficient between two ranks for entire dataset is 0.473. This study provides a multidimensional framework to assess the impact of academic papers, suggesting that future evaluations should consider not only the number of citations but also their context.
{"title":"Article ranking with location-based weight in contextual citation network","authors":"Jong Hee Jeon, Jason J. Jung","doi":"10.1016/j.joi.2024.101591","DOIUrl":"10.1016/j.joi.2024.101591","url":null,"abstract":"<div><p>This paper proposes a method to evaluate academic impact that focuses on spatial context in which citations occur in sections of citing papers. Previous studies measured impact of papers using external factors such as journals, time, and authors. However, these methods overlooks context of citations, leading to problem of treating papers with same citation counts equivalently. To overcome this issue, we designed a citation network by reflecting on the spatial context in which cited papers are cited in the citing paper and measured their impact. Spatial context is defined by the specific section of the citing paper (Introduction, Method, Result, Discussion, Conclusion) where the citation appears. We collected 818 citing papers and 13,257 cited papers from 2013–2022 from Journal of Informetrics and constructed a context-reflected citation network. Further, we utilized CRITIC method and weighted PageRank algorithm for measuring section-specific weights and impact. Results obtained in this study suggest that the impact of cited papers varies significantly depending on the section context in which they appear. We use Kendall <em>τ</em> coefficient for analyzing correlation between “times cited” rankings and contextual PageRank. The Kendall <em>τ</em> coefficient between two ranks for entire dataset is 0.473. This study provides a multidimensional framework to assess the impact of academic papers, suggesting that future evaluations should consider not only the number of citations but also their context.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241102","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}
Pub Date : 2024-09-14DOI: 10.1016/j.joi.2024.101590
Dengsheng Wu , Shuwen Wang , Weixuan Xu , Jianping Li
Conference-journal articles, which are expanded versions of conference proceedings papers, play an essential role in disseminating scientific knowledge but remain understudied. In the context of increasingly stringent research evaluation systems, this study focuses on conference-journal articles, examining the effectiveness of journals in selecting conference-derived publications. We also explore the factors influencing the citations of conference-journal articles. Here, we focused on Physics, analyzing 59,329 conference-journal articles published between 2012 and 2020, matched with general journal articles and conference proceedings papers based on the conference and journal. Results show that conference-journal articles receive significantly more citations than conference proceedings papers but fewer than general journal articles. Conference-journal articles in special issues receive fewer citations than those in regular issues. A U-shaped pattern emerges between the duration from the conference convening to the journal publication and the citation. We also found that conferences with sponsorship and those held in OECD member countries are more likely to produce highly cited conference-journal articles. Additionally, results indicate that conferences held in the USA, Japan, France, China, and Poland produce the most conference-journal articles, with articles from conferences in the USA, Japan, and France receiving relatively high citation counts. In contrast, articles from conferences held in China and Poland receive relatively low citation counts. This research provides valuable insights for academic conference committees, journal managers, and conference participants.
会议期刊论文是会议论文集论文的扩充版，在传播科学知识方面发挥着重要作用，但对其的研究仍然不足。在科研评价体系日益严格的背景下，本研究聚焦于会议期刊论文，考察期刊在选择会议衍生出版物时的有效性。我们还探讨了影响会议期刊论文被引用的因素。在此，我们以物理学为重点，分析了2012年至2020年间发表的59329篇会议期刊论文，并根据会议和期刊与普通期刊论文和会议论文集论文进行了匹配。结果显示，会议期刊论文的引用次数明显多于会议论文集论文，但少于普通期刊论文。特刊中的会议期刊论文比普通期刊论文获得的引用更少。从会议召开到期刊发表的持续时间与引用之间呈现出 U 型模式。我们还发现，有赞助的会议和在经合组织成员国举行的会议更有可能产生高引用率的会议期刊论文。此外，结果表明，在美国、日本、法国、中国和波兰举行的会议产生的会议期刊论文最多，其中美国、日本和法国会议的文章被引用次数相对较高。相比之下，在中国和波兰举行的会议上发表的文章的引用次数相对较低。这项研究为学术会议委员会、期刊管理者和会议参与者提供了有价值的见解。
{"title":"Do conference-journal articles receive more citations? A case study in physics","authors":"Dengsheng Wu , Shuwen Wang , Weixuan Xu , Jianping Li","doi":"10.1016/j.joi.2024.101590","DOIUrl":"10.1016/j.joi.2024.101590","url":null,"abstract":"<div><p>Conference-journal articles, which are expanded versions of conference proceedings papers, play an essential role in disseminating scientific knowledge but remain understudied. In the context of increasingly stringent research evaluation systems, this study focuses on conference-journal articles, examining the effectiveness of journals in selecting conference-derived publications. We also explore the factors influencing the citations of conference-journal articles. Here, we focused on Physics, analyzing 59,329 conference-journal articles published between 2012 and 2020, matched with general journal articles and conference proceedings papers based on the conference and journal. Results show that conference-journal articles receive significantly more citations than conference proceedings papers but fewer than general journal articles. Conference-journal articles in special issues receive fewer citations than those in regular issues. A U-shaped pattern emerges between the duration from the conference convening to the journal publication and the citation. We also found that conferences with sponsorship and those held in OECD member countries are more likely to produce highly cited conference-journal articles. Additionally, results indicate that conferences held in the USA, Japan, France, China, and Poland produce the most conference-journal articles, with articles from conferences in the USA, Japan, and France receiving relatively high citation counts. In contrast, articles from conferences held in China and Poland receive relatively low citation counts. This research provides valuable insights for academic conference committees, journal managers, and conference participants.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228543","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}
Novelty is a critical characteristic of innovative scientific articles, and accurately identifying novelty can facilitate the early detection of scientific breakthroughs. However, existing methods for measuring novelty have two main limitations: (1) Metadata-based approaches, such as citation analysis, are retrospective and do not alleviate the pressures of the peer review process or enable timely tracking of scientific progress; (2) Content-based methods have not adequately addressed the inherent uncertainty between the qualitative concept of novelty and the textual representation of papers. To address these issues, we propose a practical and effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model, referred to as MNSA-ITMCM. In this framework, papers are represented as topic combinations, and novelty is reflected in the organic reorganization of these topics. We use the BERTopic model to generate semantically informed topics, and then apply a topic selection algorithm based on maximum marginal relevance to obtain a topic combination that balances similarity and diversity. Furthermore, we leverage the cloud model from fuzzy mathematics to quantify novelty, overcoming the uncertainty inherent in natural language expression and topic modeling to improve the accuracy of novelty measurement. To validate the effectiveness of our framework, we conducted empirical evaluations on papers from the Cell 2021 journal (biomedical domain) and the ICLR 2023 conference (computer science domain). Through correlation analysis and prediction error analysis, our framework demonstrated the ability to identify different types of novel papers and accurately predict their novelty levels. The proposed framework is applicable across diverse scientific disciplines and publication venues, benefiting researchers, librarians, science evaluation agencies, policymakers, and funding organizations by improving the efficiency and comprehensiveness of identifying novelty research.
{"title":"An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model","authors":"Zhongyi Wang , Haoxuan Zhang , Jiangping Chen , Haihua Chen","doi":"10.1016/j.joi.2024.101587","DOIUrl":"10.1016/j.joi.2024.101587","url":null,"abstract":"<div><p>Novelty is a critical characteristic of innovative scientific articles, and accurately identifying novelty can facilitate the early detection of scientific breakthroughs. However, existing methods for measuring novelty have two main limitations: (1) Metadata-based approaches, such as citation analysis, are retrospective and do not alleviate the pressures of the peer review process or enable timely tracking of scientific progress; (2) Content-based methods have not adequately addressed the inherent uncertainty between the qualitative concept of novelty and the textual representation of papers. To address these issues, we propose a practical and effective framework for <strong>m</strong>easuring the <strong>n</strong>ovelty of <strong>s</strong>cientific <strong>a</strong>rticles through <strong>i</strong>ntegrated <strong>t</strong>opic <strong>m</strong>odeling and <strong>c</strong>loud <strong>m</strong>odel, referred to as <strong>MNSA-ITMCM</strong>. In this framework, papers are represented as topic combinations, and novelty is reflected in the organic reorganization of these topics. We use the BERTopic model to generate semantically informed topics, and then apply a topic selection algorithm based on maximum marginal relevance to obtain a topic combination that balances similarity and diversity. Furthermore, we leverage the cloud model from fuzzy mathematics to quantify novelty, overcoming the uncertainty inherent in natural language expression and topic modeling to improve the accuracy of novelty measurement. To validate the effectiveness of our framework, we conducted empirical evaluations on papers from the Cell 2021 journal (biomedical domain) and the ICLR 2023 conference (computer science domain). Through correlation analysis and prediction error analysis, our framework demonstrated the ability to identify different types of novel papers and accurately predict their novelty levels. The proposed framework is applicable across diverse scientific disciplines and publication venues, benefiting researchers, librarians, science evaluation agencies, policymakers, and funding organizations by improving the efficiency and comprehensiveness of identifying novelty research.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171980","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}
Pub Date : 2024-09-09DOI: 10.1016/j.joi.2024.101584
Yingqun Li , Ningyuan Song , Yu Shen , Lei Pei
Scientific innovation serves as the driving force behind societal progress. In contrast to conservative innovation, disruptive innovation reshapes scientific paradigms and trajectories, significantly influencing both the scientific community and societal development. This study employs an extensive empirical dataset to explore the potential of disruptive innovation to enhance the societal visibility of scientific research. Our research reveals that disruptive innovation significantly enhances societal visibility, increasing it by 11.96% compared to consolidating innovation. Furthermore, disruptive innovation does not directly lead to early-stage "breakthroughs" in scientific endeavors, but it does have a notable "acceleration" effect on societal visibility. Particularly striking is its ability to promote visibility of scientific research on social media platforms such as Twitter and blogs. However, its influence is insignificant in news articles and policy documents. This phenomenon may be attributed to the high-risk nature of disruptive innovation, which conflicts with the high level of trust, professionalism, and certainty sought in news and policy. This study carries essential implications for selecting innovative directions, the channels through which innovation is disseminated, and the formulation of science policies.
{"title":"Exploring the potential of disruptive innovation in the social sciences: A quantitative study of its impact on societal visibility","authors":"Yingqun Li , Ningyuan Song , Yu Shen , Lei Pei","doi":"10.1016/j.joi.2024.101584","DOIUrl":"10.1016/j.joi.2024.101584","url":null,"abstract":"<div><p>Scientific innovation serves as the driving force behind societal progress. In contrast to conservative innovation, disruptive innovation reshapes scientific paradigms and trajectories, significantly influencing both the scientific community and societal development. This study employs an extensive empirical dataset to explore the potential of disruptive innovation to enhance the societal visibility of scientific research. Our research reveals that disruptive innovation significantly enhances societal visibility, increasing it by 11.96% compared to consolidating innovation. Furthermore, disruptive innovation does not directly lead to early-stage \"breakthroughs\" in scientific endeavors, but it does have a notable \"acceleration\" effect on societal visibility. Particularly striking is its ability to promote visibility of scientific research on social media platforms such as Twitter and blogs. However, its influence is insignificant in news articles and policy documents. This phenomenon may be attributed to the high-risk nature of disruptive innovation, which conflicts with the high level of trust, professionalism, and certainty sought in news and policy. This study carries essential implications for selecting innovative directions, the channels through which innovation is disseminated, and the formulation of science policies.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157529","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}
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