Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the fie...Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.展开更多
Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the un...Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.展开更多
Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/metho...Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.展开更多
基金supported by grants from the National Natural Science Foundation of China No.NSFC62006109 and NSFC12031005the 13th Five-year plan for Education Science Funding of Guangdong Province No.2021GXJK349,No.2020GXJK457the Stable Support Plan Program of Shenzhen Natural Science Fund No.20220814165010001.
文摘Purpose:This study focuses on understanding the collaboration relationships among mathematicians,particularly those esteemed as elites,to reveal the structures of their communities and evaluate their impact on the field of mathematics.Design/methodology/approach:Two community detection algorithms,namely Greedy Modularity Maximization and Infomap,are utilized to examine collaboration patterns among mathematicians.We conduct a comparative analysis of mathematicians’centrality,emphasizing the influence of award-winning individuals in connecting network roles such as Betweenness,Closeness,and Harmonic centrality.Additionally,we investigate the distribution of elite mathematicians across communities and their relationships within different mathematical sub-fields.Findings:The study identifies the substantial influence exerted by award-winning mathematicians in connecting network roles.The elite distribution across the network is uneven,with a concentration within specific communities rather than being evenly dispersed.Secondly,the research identifies a positive correlation between distinct mathematical sub-fields and the communities,indicating collaborative tendencies among scientists engaged in related domains.Lastly,the study suggests that reduced research diversity within a community might lead to a higher concentration of elite scientists within that specific community.Research limitations:The study’s limitations include its narrow focus on mathematicians,which may limit the applicability of the findings to broader scientific fields.Issues with manually collected data affect the reliability of conclusions about collaborative networks.Practical implications:This study offers valuable insights into how elite mathematicians collaborate and how knowledge is disseminated within mathematical circles.Understanding these collaborative behaviors could aid in fostering better collaboration strategies among mathematicians and institutions,potentially enhancing scientific progress in mathematics.Originality/value:The study adds value to understanding collaborative dynamics within the realm of mathematics,offering a unique angle for further exploration and research.
基金This work was supported by grants from the National Natural Science Foundation of China,with No.NSFC62006109 and NSFC12031005.
文摘Purpose:In recent decades,with the availability of large-scale scientific corpus datasets,difference-in-difference(DID)is increasingly used in the science of science and bibliometrics studies.DID method outputs the unbiased estimation on condition that several hypotheses hold,especially the common trend assumption.In this paper,we gave a systematic demonstration of DID in the science of science,and the potential ways to improve the accuracy of DID method.Design/methodology/approach:At first,we reviewed the statistical assumptions,the model specification,and the application procedures of DID method.Second,to improve the necessary assumptions before conducting DID regression and the accuracy of estimation,we introduced some matching techniques serving as the pre-selecting step for DID design by matching control individuals who are equivalent to those treated ones on observational variables before the intervention.Lastly,we performed a case study to estimate the effects of prizewinning on the scientific performance of Nobel laureates,by comparing the yearly citation impact after the prizewinning year between Nobel laureates and their prizewinning-work coauthors.Findings:We introduced the procedures to conduct a DID estimation and demonstrated the effectiveness to use matching method to improve the results.As a case study,we found that there are no significant increases in citations for Nobel laureates compared to their prizewinning coauthors.Research limitations:This study ignored the rigorous mathematical deduction parts of DID,while focused on the practical parts.Practical implications:This work gives experimental practice and potential guidelines to use DID method in science of science and bibliometrics studies.Originality/value:This study gains insights into the usage of econometric tools in science of science.
基金supported by grants from Shenzhen Polytechnic University Research(Fund No.6025310042 K)the National Natural Science Foundation of China(No.NSFC62006109 and NSFC12031005).
文摘Purpose:This study investigates the impact of domestic mobility on Chinese scientists’academic performance and explores the predictors influencing their chances of moving to more prestigious institutions.Design/methodology/approach:Using publication and affiliation data from OpenAlex,we identified 2,896 scientists who relocated between cities in China from 2014 to 2017.We applied propensity score matching(PSM)to compare their academic outcomes post-mobility with a matched group of non-mobile peers.Multiple performance metrics were examined,including publication count,citation impact,number of collaborators,and university prestige.Ordered logistic regression was used to analyze factors influencing moves to higher-level institutions.Findings:Mobility enhances collaboration by increasing the number of coauthors but is associated with a short-term decline in citation impact.Scientists were more likely to move to lower-prestige universities.However,prior collaboration breadth and citation count positively predicted transitions to more prestigious institutions,while the number of publications did not.Research limitations:This study focuses on intra-national mobility within China from 2014 to 2017 and relies on quantitative data,lacking personal or qualitative variables such as gender,discipline-specific norms,or institutional culture.Data coverage for Chinese-language publications may also be limited.Practical implications:This research provides insights into academic hiring patterns and the trade-offs involved in scientist mobility.It offers valuable guidance for institutions aiming to enhance faculty recruitment and retention,as well as for researchers considering career transitions.Originality/value:This is a quantitative analysis of domestic scientist mobility in China using matched comparison and multi-dimensional academic indicators.The integration of university prestige metrics(Double First-Class and citation-based rankings)offers a nuanced view of career dynamics within the Chinese higher education system.
