Pub Date : 2025-09-12DOI: 10.1016/j.joi.2025.101726
Guoyang Rong , Ying Chen , Feicheng Ma , Thorsten Koch
This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify critical years for interdisciplinary citations (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981–2002), marked by sporadic and limited interdisciplinary activity; Period II (2003–2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017–2020), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary.
{"title":"Exploring interdisciplinary research trends through critical years for interdisciplinary citation","authors":"Guoyang Rong , Ying Chen , Feicheng Ma , Thorsten Koch","doi":"10.1016/j.joi.2025.101726","DOIUrl":"10.1016/j.joi.2025.101726","url":null,"abstract":"<div><div>This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify <em>critical years for interdisciplinary citations</em> (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981–2002), marked by sporadic and limited interdisciplinary activity; Period II (2003–2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017–2020), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101726"},"PeriodicalIF":3.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048979","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 : 2025-09-10DOI: 10.1016/j.joi.2025.101728
Guo Chen , Han Sun , Xianzu Liu , Lu Xiao
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.
{"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}
Pub Date : 2025-09-10DOI: 10.1016/j.joi.2025.101730
Xian Li , Haixing Du , Yi Bu , Mingshu Ai , Junjie Huang , Tao Jia
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 OpenAlex 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.
{"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}
Pub Date : 2025-09-03DOI: 10.1016/j.joi.2025.101723
Xifeng Gu, An Zeng
Co-authorship has become more common, yet most studies focus on paper-to-paper citation patterns, overlooking the role of group collaborations. Our study explores how research group structures influence citation patterns, using a Co-Authorship Citation Network (CACN) based on the SciSciNet dataset, which includes 134 million publications and over 1.5 billion citation links. As time progresses, repeated citations within groups become more pronounced, with a 30% higher rate of repeated citations in 2000 compared to 1950. Disruptive papers are cited repeatedly by fewer groups, while impactful papers attract citations from more groups. Additionally, fields like Physics and Geology show higher rates of repeated citations, while Political Science and Sociology exhibit broader citation behaviors. This research enables researchers, institutions, and publishers to better understand group citation behaviors and improve knowledge dissemination across disciplines.
{"title":"The increasing dominance of repeated citations from collaborative research groups in science","authors":"Xifeng Gu, An Zeng","doi":"10.1016/j.joi.2025.101723","DOIUrl":"10.1016/j.joi.2025.101723","url":null,"abstract":"<div><div>Co-authorship has become more common, yet most studies focus on paper-to-paper citation patterns, overlooking the role of group collaborations. Our study explores how research group structures influence citation patterns, using a Co-Authorship Citation Network (CACN) based on the SciSciNet dataset, which includes 134 million publications and over 1.5 billion citation links. As time progresses, repeated citations within groups become more pronounced, with a 30% higher rate of repeated citations in 2000 compared to 1950. Disruptive papers are cited repeatedly by fewer groups, while impactful papers attract citations from more groups. Additionally, fields like Physics and Geology show higher rates of repeated citations, while Political Science and Sociology exhibit broader citation behaviors. This research enables researchers, institutions, and publishers to better understand group citation behaviors and improve knowledge dissemination across disciplines.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101723"},"PeriodicalIF":3.5,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144932825","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 : 2025-08-31DOI: 10.1016/j.joi.2025.101727
Francesco Branda , Massimo Ciccozzi , Fabio Scarpa
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.
{"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}
Pub Date : 2025-08-29DOI: 10.1016/j.joi.2025.101722
Jaemin Chung , Janghyeok Yoon , Jaewoong Choi
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.
{"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}
Pub Date : 2025-08-28DOI: 10.1016/j.joi.2025.101725
Giovanni Abramo , Tindaro Cicero , Ciriaco Andrea D’Angelo
This study aims to improve the accuracy of long-term citation impact prediction by integrating early citation counts, Mendeley readership, and various non-scientific factors, such as journal impact factor, authorship and reference list characteristics, funding and open-access status. Traditional citation-based models often fall short by relying solely on early citations, which may not capture broader indicators of a publication’s potential influence. By incorporating non-scientific predictors, this model provides a more nuanced and comprehensive framework that outperforms existing models in predicting long-term impact. Using a dataset of Italian-authored publications from the Web of Science, regression models were developed to evaluate the impact of these predictors over time. Results indicate that early citations and Mendeley readership are significant predictors of long-term impact, with additional contributions from factors like authorship diversity and journal impact factor. The study finds that open-access status and funding have diminishing predictive power over time, suggesting their influence is primarily short-term. This model benefits various stakeholders, including funders and policymakers, by offering timely and more accurate assessments of emerging research. Future research could extend this model by incorporating broader altmetrics and expanding its application to other disciplines and regions. The study concludes that integrating non-citation-based factors with early citations captures a more complex view of scholarly impact, aligning better with real-world research influence.
本研究旨在通过整合早期引文计数、Mendeley读者数以及期刊影响因子、作者和参考文献列表特征、资助和开放获取状况等多种非科学因素，提高长期引文影响预测的准确性。传统的基于引用的模型往往仅仅依赖于早期引用，这可能无法捕捉到出版物潜在影响力的更广泛指标。通过纳入非科学预测因素，该模型提供了一个更细致和全面的框架，在预测长期影响方面优于现有模型。利用来自Web of Science的由意大利人撰写的出版物的数据集，开发了回归模型来评估这些预测因子随时间的影响。结果表明，早期引用和Mendeley读者群是长期影响的重要预测因子，作者多样性和期刊影响因子等因素也有贡献。研究发现，随着时间的推移，开放获取的地位和资助的预测能力正在减弱，这表明它们的影响主要是短期的。这种模式通过对新兴研究提供及时和更准确的评估，使包括资助者和决策者在内的各种利益攸关方受益。未来的研究可以通过纳入更广泛的替代指标并将其应用于其他学科和地区来扩展这一模型。该研究的结论是，将非引用因素与早期引用相结合，可以更复杂地反映学术影响，更好地与现实世界的研究影响保持一致。
{"title":"Enhancing the prediction of publications’ long-term impact using early citations, readerships, and non-scientific factors","authors":"Giovanni Abramo , Tindaro Cicero , Ciriaco Andrea D’Angelo","doi":"10.1016/j.joi.2025.101725","DOIUrl":"10.1016/j.joi.2025.101725","url":null,"abstract":"<div><div>This study aims to improve the accuracy of long-term citation impact prediction by integrating early citation counts, Mendeley readership, and various non-scientific factors, such as journal impact factor, authorship and reference list characteristics, funding and open-access status. Traditional citation-based models often fall short by relying solely on early citations, which may not capture broader indicators of a publication’s potential influence. By incorporating non-scientific predictors, this model provides a more nuanced and comprehensive framework that outperforms existing models in predicting long-term impact. Using a dataset of Italian-authored publications from the Web of Science, regression models were developed to evaluate the impact of these predictors over time. Results indicate that early citations and Mendeley readership are significant predictors of long-term impact, with additional contributions from factors like authorship diversity and journal impact factor. The study finds that open-access status and funding have diminishing predictive power over time, suggesting their influence is primarily short-term. This model benefits various stakeholders, including funders and policymakers, by offering timely and more accurate assessments of emerging research. Future research could extend this model by incorporating broader altmetrics and expanding its application to other disciplines and regions. The study concludes that integrating non-citation-based factors with early citations captures a more complex view of scholarly impact, aligning better with real-world research influence.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101725"},"PeriodicalIF":3.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908249","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 : 2025-08-06DOI: 10.1016/j.joi.2025.101711
Juan Gorraiz
This article explores the often-overlooked religious and philosophical roots of bibliometrics. Drawing on motifs from the Hebrew Bible and Christian theology—including the Chosen People, the Matthew Effect, David’s census, and the Tower of Babel—it argues that bibliometrics, while presented as a neutral and quantitative science, is deeply embedded in cultural narratives of worth, selection, judgment, and transcendence. The paper reflects on how metaphors of purification, idolatry, and incommensurability help us understand both the power and the limits of bibliometric practices. Rather than offering prescriptive rules, it concludes with a series of critical reflections that emphasize humility, interpretative context, and the need to continually question the values embedded in metrics—reminding us that in bibliometrics, as in faith, what we measure may never fully capture what truly matters.
{"title":"From 'sleeping beauties' to 'rising stars': The religious and philosophical roots of bibliometrics","authors":"Juan Gorraiz","doi":"10.1016/j.joi.2025.101711","DOIUrl":"10.1016/j.joi.2025.101711","url":null,"abstract":"<div><div>This article explores the often-overlooked religious and philosophical roots of bibliometrics. Drawing on motifs from the Hebrew Bible and Christian theology—including the Chosen People, the Matthew Effect, David’s census, and the Tower of Babel—it argues that bibliometrics, while presented as a neutral and quantitative science, is deeply embedded in cultural narratives of worth, selection, judgment, and transcendence. The paper reflects on how metaphors of purification, idolatry, and incommensurability help us understand both the power and the limits of bibliometric practices. Rather than offering prescriptive rules, it concludes with a series of critical reflections that emphasize humility, interpretative context, and the need to continually question the values embedded in metrics—reminding us that in bibliometrics, as in faith, what we measure may never fully capture what truly matters.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 4","pages":"Article 101711"},"PeriodicalIF":3.5,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780237","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 : 2025-08-01DOI: 10.1016/j.joi.2025.101702
Jonas Lindahl , Rickard Danell , Kaylee Litson , David F. Feldon
This study examines the sex productivity gap among doctoral students in Sweden using a comparative design. It focuses particularly on how the gap increases at the higher end of the productivity distribution, with men consistently publishing more than women. The study is based on a large dataset of 10,804 doctoral students who graduated between 2010 and 2019 in the research areas of the natural sciences, engineering and technology, medical and health sciences, and the social sciences. By applying multiple quantile regression analysis, we were able to conduct a nuanced analysis of the sex productivity gap across the whole productivity distribution. Results indicate a consistent productivity gap by sex across all research areas and that the gap increases towards the higher end of the distribution, i.e., the sex differences in productivity increase among the top performers. However, the comparison of research areas revealed some heterogeneity. In engineering and technology, the increasing sex gap levels off in the middle of the distribution but takes a leap at the extreme tail. In the social sciences, the gap peaks just before the extreme end of the distribution and then starts decreasing. The natural sciences and medical and health sciences show a more gradual increase in the gap towards the higher end. Taking into account the Swedish context – with its widespread adoption of the collective model of doctoral education and the thesis-by-publication format – our main conclusions are: (1) there exists a consistent sex productivity gap across all studied research areas, and (2) the increasing sex gap at the upper end of the productivity distribution, commonly seen in later career stages, can already be observed during doctoral studies.
{"title":"Sex differences in research productivity among doctoral students in Sweden: A quantile regression approach","authors":"Jonas Lindahl , Rickard Danell , Kaylee Litson , David F. Feldon","doi":"10.1016/j.joi.2025.101702","DOIUrl":"10.1016/j.joi.2025.101702","url":null,"abstract":"<div><div>This study examines the sex productivity gap among doctoral students in Sweden using a comparative design. It focuses particularly on how the gap increases at the higher end of the productivity distribution, with men consistently publishing more than women. The study is based on a large dataset of 10,804 doctoral students who graduated between 2010 and 2019 in the research areas of the natural sciences, engineering and technology, medical and health sciences, and the social sciences. By applying multiple quantile regression analysis, we were able to conduct a nuanced analysis of the sex productivity gap across the whole productivity distribution. Results indicate a consistent productivity gap by sex across all research areas and that the gap increases towards the higher end of the distribution, i.e., the sex differences in productivity increase among the top performers. However, the comparison of research areas revealed some heterogeneity. In engineering and technology, the increasing sex gap levels off in the middle of the distribution but takes a leap at the extreme tail. In the social sciences, the gap peaks just before the extreme end of the distribution and then starts decreasing. The natural sciences and medical and health sciences show a more gradual increase in the gap towards the higher end. Taking into account the Swedish context – with its widespread adoption of the collective model of doctoral education and the thesis-by-publication format – our main conclusions are: (1) there exists a consistent sex productivity gap across all studied research areas, and (2) the increasing sex gap at the upper end of the productivity distribution, commonly seen in later career stages, can already be observed during doctoral studies.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 3","pages":"Article 101702"},"PeriodicalIF":3.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829272","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 : 2025-08-01DOI: 10.1016/j.joi.2025.101712
Renli Wu , Ruiyang Chen , Lu An , Chuanfu Chen
With rapid technological advancements and societal changes, the field of Library and Information Science (LIS) is dramatically evolving. To capture these shifts, we analyzed over 140,000 LIS publications ranging from 1990 to 2023, examining the discipline’s research evolution across three semantic dimensions: library, representing the historical foundation and institutional infrastructure of LIS; people, representing the core interacting participants and human-centered focus of LIS; and algorithm, representing the methodological advancements driven by emerging technologies in LIS. Utilizing Doc2Vec with a multi-label joint training scheme, we created a consistent embedding space for various LIS entities, including research terms, papers, journals, and countries. By mapping these entities onto a unified framework underpinned by three anchored dimensions, we reveal that the publications of the library dimension, dominant since the 1990s, have declined after 2011, reflected in the focus shifts of LIS research, journal clusters, and nations. Concurrently, LIS research has gravitated toward the people dimension, with people-related studies evolving into a more independent branch. The algorithm dimension is rapidly emerging, with journals more closely associated with it exhibiting higher impact factors, and the research centroids of journals and countries are converging toward it. However, algorithm-dominated research is increasingly detached from the other two dimensions, especially the library. Additionally, developed countries prioritize the research related to library and people dimensions, while developing countries exhibit a stronger emphasis on algorithms-focused publications. To ensure robustness, we further validated our results using a recent ModernBERT model fine-tuned for the LIS context. The findings reveal the developmental dynamics and potential fragmentation within LIS, offering insights for scholars, journals, institutions, and policymakers.
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