Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the...Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.展开更多
It is important to effectively identify the data value of open source scientific and technological information and to help intelligence analysts select high-value data from a large number of open-source scientific and...It is important to effectively identify the data value of open source scientific and technological information and to help intelligence analysts select high-value data from a large number of open-source scientific and technological information. The data value evaluation methods of scientific and technological information is proposed in the open source environment. According to the characteristics of the methods, the data value evaluation methods were divided into the following three aspects: research on data value evaluation methods based on information metrology, research on data value evaluation methods based on economic perspective and research on data value assessment methods based on text analysis. For each method, it indicated the main ideas, application scenarios, advantages and disadvantages.展开更多
State-owned enterprises(SOEs)are important components of the Chinese economy.Although SOEs are generally considered inefficient in operations,China’s economy,which relies heavily on SOEs,has been highly successful ov...State-owned enterprises(SOEs)are important components of the Chinese economy.Although SOEs are generally considered inefficient in operations,China’s economy,which relies heavily on SOEs,has been highly successful over the last four decades.This indicates the importance of SOEs in China’s past and future economic success.Therefore,in this study,we review the literature on economic theories and 40 years of practice of Chinese SOEs and discuss implications for future research.Our review consists of four parts:the theories of SOEs and their reform,the performance and financing strategies of SOEs,corporate governance in SOEs,and corporate social responsibility in SOEs.展开更多
基金supported by the General Projects of ISTIC Innovation Foundation“Problem innovation solution mining based on text generation model”(MS2024-03).
文摘Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.
文摘It is important to effectively identify the data value of open source scientific and technological information and to help intelligence analysts select high-value data from a large number of open-source scientific and technological information. The data value evaluation methods of scientific and technological information is proposed in the open source environment. According to the characteristics of the methods, the data value evaluation methods were divided into the following three aspects: research on data value evaluation methods based on information metrology, research on data value evaluation methods based on economic perspective and research on data value assessment methods based on text analysis. For each method, it indicated the main ideas, application scenarios, advantages and disadvantages.
基金financial support of the National Social Science Fund of China Key Research Project(No.17ZDA086):Research on Reforms and Innovations of Monitoring System in State-Owned Enterprises.
文摘State-owned enterprises(SOEs)are important components of the Chinese economy.Although SOEs are generally considered inefficient in operations,China’s economy,which relies heavily on SOEs,has been highly successful over the last four decades.This indicates the importance of SOEs in China’s past and future economic success.Therefore,in this study,we review the literature on economic theories and 40 years of practice of Chinese SOEs and discuss implications for future research.Our review consists of four parts:the theories of SOEs and their reform,the performance and financing strategies of SOEs,corporate governance in SOEs,and corporate social responsibility in SOEs.
