Purpose:This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network.Design/methodology/approach:This paper investigated the di...Purpose:This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network.Design/methodology/approach:This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network,a knowledge-representative framework that can model relationships among all variables in the network.We defined the variables required for Bayesian networks modeling,calculated the values of each variable based Aminer dataset(a literature data set in the field of computer science),learned the Bayesian network and derived some findings based on network inference.Findings:The study found that conferences are more attractive to senior scholars,the academic impact of conference papers is slightly higher than journal papers,and it is uncertain whether conference papers are more innovative than journal papers.Research limitations:The study was limited to the field of computer science and employed Aminer dataset as the sample.Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter.Practical implications:By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics,the study offers valuable insights that may enhance researchers’understanding of the differences between journal and conference in computer science.Originality/value:Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science.Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors,such as authors,citations,h-index and others.Those studies were carried out from different(independent)perspectives,lacking a systematic examination of the connections and interactions between multiple perspectives.This paper supplements this deficiency based on Bayesian network modeling.展开更多
Purpose： This paper proposes an expert assignment method for scientific project review that considers both accuracy and impartiality. As impartial and accurate peer review is extremely important to ensure the quality...Purpose： This paper proposes an expert assignment method for scientific project review that considers both accuracy and impartiality. As impartial and accurate peer review is extremely important to ensure the quality and feasibility of scientific projects, enhanced methods for managing the process are needed. Design/methodology/approach： To ensure both accuracy and impartiality, we design four criteria, the reviewers＇fitness degree, research intensity, academic association, and potential conflict of interest, to express the characteristics of an appropriate peer review expert. We first formalize the expert assignment problem as an optimization problem based on the designed criteria, and then propose a randomized algorithm to solve the expert assignment problem of identifying reviewer adequacy. Findings： Simulation results show that the proposed method is quite accurate and impartial during expert assignment. Research limitations： Although the criteria used in this paper can properly show the characteristics of a good and appropriate peer review expert, more criteria/conditions can be included in the proposed scheme to further enhance accuracy and impartiality of the expert assignment. Practical implications： The proposed method can help project funding agencies （e.g. the National Natural Science Foundation of China） find better experts for project peer review. OriginaUty/value： To the authors＇ knowledge, this is the first publication that proposes an algorithm that applies an impartial approach to the project review expert assignment process. The simulation results show the effectiveness of the proposed method.展开更多
基金The work of this paper is supported by the Chinese Academy of Sciences Literature and Information capacity building project,Youth Innovation Promotion Association of Chinese Academy of Sciences(No.2019176).
文摘Purpose:This paper aims to investigate the differences between conference papers and journal papers in the field of computer science based on Bayesian network.Design/methodology/approach:This paper investigated the differences between conference papers and journal papers in the field of computer science based on Bayesian network,a knowledge-representative framework that can model relationships among all variables in the network.We defined the variables required for Bayesian networks modeling,calculated the values of each variable based Aminer dataset(a literature data set in the field of computer science),learned the Bayesian network and derived some findings based on network inference.Findings:The study found that conferences are more attractive to senior scholars,the academic impact of conference papers is slightly higher than journal papers,and it is uncertain whether conference papers are more innovative than journal papers.Research limitations:The study was limited to the field of computer science and employed Aminer dataset as the sample.Further studies involving more diverse datasets and different fields could provide a more complete picture of the matter.Practical implications:By demonstrating that Bayesian networks can effectively analyze issues in Scientometrics,the study offers valuable insights that may enhance researchers’understanding of the differences between journal and conference in computer science.Originality/value:Academic conferences play a crucial role in facilitating scholarly exchange and knowledge dissemination within the field of computer science.Several studies have been conducted to examine the distinctions between conference papers and journal papers in terms of various factors,such as authors,citations,h-index and others.Those studies were carried out from different(independent)perspectives,lacking a systematic examination of the connections and interactions between multiple perspectives.This paper supplements this deficiency based on Bayesian network modeling.
基金supported by the National Natural Science Foundation of China under the grant (No.7160325)the Young Talent-Field Frontier Project of Wuhan Documentation and Information Center,Chinese Academy of Sciences
文摘Purpose： This paper proposes an expert assignment method for scientific project review that considers both accuracy and impartiality. As impartial and accurate peer review is extremely important to ensure the quality and feasibility of scientific projects, enhanced methods for managing the process are needed. Design/methodology/approach： To ensure both accuracy and impartiality, we design four criteria, the reviewers＇fitness degree, research intensity, academic association, and potential conflict of interest, to express the characteristics of an appropriate peer review expert. We first formalize the expert assignment problem as an optimization problem based on the designed criteria, and then propose a randomized algorithm to solve the expert assignment problem of identifying reviewer adequacy. Findings： Simulation results show that the proposed method is quite accurate and impartial during expert assignment. Research limitations： Although the criteria used in this paper can properly show the characteristics of a good and appropriate peer review expert, more criteria/conditions can be included in the proposed scheme to further enhance accuracy and impartiality of the expert assignment. Practical implications： The proposed method can help project funding agencies （e.g. the National Natural Science Foundation of China） find better experts for project peer review. OriginaUty/value： To the authors＇ knowledge, this is the first publication that proposes an algorithm that applies an impartial approach to the project review expert assignment process. The simulation results show the effectiveness of the proposed method.
