Purpose:The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations....Purpose:The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.Design/methodology/approach:We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts.We also remove the target paper in calculating the contribution of co-citations.Following previous studies,we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society(APS)and the Microsoft Academic Graph(MAG)dataset to test the accuracy of our proposed method(NCCAS).In addition,we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS.Finding:Compared with the state-of-the-art methods,NCCAS gives the most accurate prediction of Nobel laureates.Furthermore,the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors.The results by NCCAS are also more robust to malicious manipulation.Finally,we perform ablation studies to show the contribution of different components in our methods.Research limitations:Due to limited ground truth on the true leading author of a work,the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers.Practical implications:NCCAS is successfully applied to a large number of publications,demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders.Originality/value:Compared with existing methods,NCCAS not only identifies the leading author of a paper more accurately,but also makes the deification more distinguishable and more robust,providing a new tool for related studies.展开更多
This paper investigates credit allocation before and after the 2003 banking system reform in China. We find that relationships between earnings quality and new short-term loans, long-term loans and total loans in list...This paper investigates credit allocation before and after the 2003 banking system reform in China. We find that relationships between earnings quality and new short-term loans, long-term loans and total loans in listed companies changed significantly after the banking system reform, especially in stateowned listed companies. Further investigation shows that due to the influence of rent-seeking, banks have eased the earnings requirements of non-stateowned listed companies. These findings enhance our understanding of the economic consequences of the banking system reform and of credit discrimination under the new regime.展开更多
基金This work was supported by University Innovation Research Group of Chongqing(No.CXQT21005).
文摘Purpose:The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.Design/methodology/approach:We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts.We also remove the target paper in calculating the contribution of co-citations.Following previous studies,we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society(APS)and the Microsoft Academic Graph(MAG)dataset to test the accuracy of our proposed method(NCCAS).In addition,we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS.Finding:Compared with the state-of-the-art methods,NCCAS gives the most accurate prediction of Nobel laureates.Furthermore,the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors.The results by NCCAS are also more robust to malicious manipulation.Finally,we perform ablation studies to show the contribution of different components in our methods.Research limitations:Due to limited ground truth on the true leading author of a work,the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers.Practical implications:NCCAS is successfully applied to a large number of publications,demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders.Originality/value:Compared with existing methods,NCCAS not only identifies the leading author of a paper more accurately,but also makes the deification more distinguishable and more robust,providing a new tool for related studies.
基金supported by the Jiangsu Province University Philosophy Social Science Fund Project entitled "Local Government Governance -Earnings Quality and Credit Allocation"(Grant No.: 2011SJD630055)
文摘This paper investigates credit allocation before and after the 2003 banking system reform in China. We find that relationships between earnings quality and new short-term loans, long-term loans and total loans in listed companies changed significantly after the banking system reform, especially in stateowned listed companies. Further investigation shows that due to the influence of rent-seeking, banks have eased the earnings requirements of non-stateowned listed companies. These findings enhance our understanding of the economic consequences of the banking system reform and of credit discrimination under the new regime.