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Analysis of Factors in Citing Scientific Papers in Policies against COVID-19 Pandemic |
Ren Chao1, Yang Menghui1,2, Li Kai1, Yang Guancan1, Lu Xiaobin1 |
1.School of Information Resource Management, Renmin University of China, Beijing 100872 2.Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, Beijing 100872 |
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Abstract In the context of the COVID-19 pandemic, the role of science in supporting policy decision-making has become increasingly important. Scientific papers have gradually become the main reference sources for policy making, and evidence-based decision-making guarantees the scientific development of policies against the COVID-19 pandemic. To explore the factors affecting the citation of scientific papers in policies and evaluate the importance of these factors from the perspective of evidence-based policy making, this study considers scientific papers as research objects and constructs an index system with three levels of influencing factors: primary indices (comprising research evidence factors, internal factors of researchers, and external environmental factors); secondary indices, comprising four important factors in citing scientific papers (namely rationality, originality, and scientific and social values); and 34 characteristics as tertiary indices. By combining five sampling and classification techniques, the accuracy of the influencing factors index system and prediction model reported here was verified. Additionally, the importance of the different factors was explored through feature importance and correlation analyses. The results show that the proposed factors index system, mainly at the three levels of papers, authors, and journals, are important factors affecting the citation of scientific papers in the policies against COVID-19. The results further show that the most important factors affecting the citation of scientific papers in policies are the academic influences of scientific papers and authors and the degree of dissemination of the papers in the news and social media. Concurrently, it was found that the mechanism of citing scientific papers in policy documents is relatively complex, and there is a need for further studies on multiple and complex factors influencing the selection of scientific papers as evidence-based reference sources in policy documents.
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Received: 21 March 2022
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1 王晰巍, 李玥琪, 刘婷艳, 等. 新冠肺炎疫情微博用户情感与主题挖掘的协同模型研究[J]. 情报学报, 2021, 40(3): 223-233. 2 池毛毛, 王俊晶, 王伟军. 突发重大疫情下基层政府信任对公民信息不披露意愿的影响机制研究[J]. 情报学报, 2021, 40(6): 666-678. 3 谢娟, 李文文, 沈鸿权, 等. 信息爆炸和信息不确定语境下的可信度判据研究——以COVID-19疫情为例[J]. 情报学报, 2021, 40(7): 714-724. 4 白彦锋, 唐艺宁. 中国应对突发公共卫生事件财税政策的对比研究[J]. 经济理论与经济管理, 2020(7): 17-29. 5 Wu J, Wang K L, He C C, et al. Characterizing the patterns of China’s policies against COVID-19: a bibliometric study[J]. Information Processing & Management, 2021, 58(4): 102562. 6 Overton. Overton help center: advice and answers from the Overton team[EB/OL]. [2022-01-01]. https://help.overton.io. 7 Balmford B, Annan J D, Hargreaves J C, et al. Cross-country comparisons of COVID-19: policy, politics and the price of life[J]. Environmental & Resource Economics, 2020, 76(4): 525-551. 8 Zhang S X, Wang Z Z, Chang R J, et al. COVID-19 containment: China provides important lessons for global response[J]. Frontiers of Medicine, 2020, 14(2): 215-219. 9 中华人民共和国国务院新闻办公室. 抗击新冠肺炎疫情的中国行动[N]. 人民日报, 2020-06-08(10). 10 任超, 杨孟辉. 隧道尽头的光芒: 公共卫生政策计量分析研究[J/OL]. 图书馆论坛, (2022-08-05). https://kns.cnki.net/kns8/Detail?sfield=fn&QueryID=0&CurRec=1&recid=&FileName=TSGL202 20804000&DbName=CAPJLAST&DbCode=CAPJ&yx=Y&pr=&UR LID=44.1306.g2.20220804.1704.004. 11 Garfield E. Citation indexing—its theory and application in science, technology, and humanities[M]. New York: John Wiley & Sons, 1979. 12 Garfield E. Validation of citation analysis[J]. Journal of the American Society for Information Science, 1997, 48(10): 962. 13 黄萃, 任弢, 张剑. 政策文献量化研究: 公共政策研究的新方向[J]. 公共管理学报, 2015, 12(2): 129-137, 158-159. 14 Yin Y A, Gao J, Jones B F, et al. Coevolution of policy and science during the pandemic[J]. Science, 2021, 371(6525): 128-130. 15 杨代福, 刘爽. 新冠疫情应对决策中的研究证据使用: 基于十个国家的定性比较分析[J]. 科学学研究, 2022, 40(2): 278-287. 16 Aksnes D W, Langfeldt L, Wouters P. Citations, citation indicators, and research quality: an overview of basic concepts and theories[J/OL]. SAGE Open, 2019, 9(1). (2019-02-07). https://doi.org/10.1177/2158244019829575. 17 Boswell C, Smith K. Rethinking policy ‘impact’: four models of research-policy relations[J]. Palgrave Communications, 2017, 3(1): 1-10. 18 Caplan N. The two-communities theory and knowledge utilization[J]. American Behavioral Scientist, 1979, 22(3): 459-470. 19 Myers K R, Tham W Y, Yin Y A, et al. Unequal effects of the COVID-19 pandemic on scientists[J]. Nature Human Behaviour, 2020, 4(9): 880-883. 20 Staniscuaski F, Reichert F, Werneck F P, et al. Impact of COVID-19 on academic mothers[J]. Science, 2020, 368(6492): 724. 21 Carr R M, Lane-Fall M B, South E, et al. Academic careers and the COVID-19 pandemic: reversing the tide[J]. Science Translational Medicine, 2021, 13(584): eabe7189. 22 Gao J, Yin Y A, Myers K R, et al. Potentially long-lasting effects of the pandemic on scientists[J]. Nature Communications, 2021, 12(1): 1-6. 23 Cheng X, Tang L, Zhou M T, et al. Coevolution of COVID-19 research and China’s policies[J]. Health Research Policy and Systems, 2021, 19(1): 121. 24 魏夏楠, 张春阳. “循证决策”30年: 发展脉络、研究现状和前沿挈领——基于国内外代表性文献的研究综述[J]. 现代管理科学, 2021(4): 26-36. 25 Pawson R. Evidence-based policy: the promise of ‘realist synthesis’[J]. Evaluation, 2002, 8(3): 340-358. 26 马小亮, 樊春良. 基于证据的政策: 思想起源、发展和启示[J]. 科学学研究, 2015, 33(3): 353-362. 27 阚道远, 梁靖宇. 欧美反智主义的兴起: 一个社会阶层结构嬗变的视角[J]. 浙江大学学报(人文社会科学版), 2022, 52(4): 82-93. 28 刘瑞, 马海群. 基于证据的开放政府数据政策制定研究[J]. 现代情报, 2019, 39(7): 128-132, 152. 29 徐宏宇. 证据在科技决策中的应用研究[J]. 现代情报, 2020, 40(9): 90-95. 30 Fang Z C, Costas R, Tian W C, et al. An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics[J]. Scientometrics, 2020, 124(3): 2519-2549. 31 余厚强, 肖婷婷, 王曰芬, 等. 政策文件替代计量指标分布特征研究[J]. 中国图书馆学报, 2017, 43(5): 57-69. 32 余厚强, 李龙飞. 政策文件替代计量指标影响因素研究[J]. 情报理论与实践, 2021, 44(7): 28-36. 33 Haunschild R, Bornmann L. How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data[J]. Scientometrics, 2017, 110(3): 1209-1216. 34 Yu H Q, Cao X T, Xiao T T, et al. How accurate are policy document mentions? A first look at the role of altmetrics database[J]. Scientometrics, 2020, 125(2): 1517-1540. 35 Kale B, Siravuri H V, Alhoori H, et al. Predicting research that will be cited in policy documents[C]// Proceedings of the 2017 ACM on Web Science Conference. New York: ACM Press, 2017: 389-390. 36 Moral-Mu?oz J A, Salazar A, Lucena-Anton D, et al. Social media attention of the ESI highly cited papers: an Altmetrics-based overview[C]// Proceedings of the International Conference on Scientometrics and Informetrics, 2019: 1734-1745. 37 陈斯斯, 刘春丽. 论文临床影响力评价及预测指标的实证研究——基于诺贝尔生理学或医学奖获得者成果的分析[J]. 情报学报, 2022, 41(2): 142-154. 38 Weis J W, Jacobson J M. Learning on knowledge graph dynamics provides an early warning of impactful research[J]. Nature Biotechnology, 2021, 39(10): 1300-1307. 39 Wang M Y, Wang Z Y, Chen G S. Which can better predict the future success of articles? Bibliometric indices or alternative metrics[J]. Scientometrics, 2019, 119(3): 1575-1595. 40 Fu L D, Aliferis C F. Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature[J]. Scientometrics, 2010, 85(1): 257-270. 41 夏琬钧, 陈晓红, 江艳萍. 学术论文引用预测研究进展[J]. 图书情报工作, 2020, 64(6): 138-145. 42 耿骞, 景然, 靳健, 等. 学术论文引用预测及影响因素分析[J]. 图书情报工作, 2018, 62(14): 29-40. 43 Bhat H S, Huang L H, Rodriguez S, et al. Citation prediction using diverse features[C]// Proceedings of the 2015 IEEE International Conference on Data Mining Workshop. IEEE, 2015: 589-596. 44 Fraser N, Brierley L, Dey G, et al. The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape[J]. PLoS Biology, 2021, 19(4): e3000959. 45 陈悦, 王智琦, 刘则渊, 等. 预印本的学术影响力研究——以arXiv自存档论文为例[J]. 情报学报, 2019, 38(8): 815-825. 46 Pal J K. Visualizing the knowledge outburst in global research on COVID-19[J]. Scientometrics, 2021, 126(5): 4173-4193. 47 Adie E, Roe W. Altmetric: enriching scholarly content with article-level discussion and metrics[J]. Learned Publishing, 2013, 26(1): 11-17. 48 Bhandari M, Guyatt G H, Kulkarni A V, et al. Perceptions of authors’ contributions are influenced by both byline order and designation of corresponding author[J]. Journal of Clinical Epidemiology, 2014, 67(9): 1049-1054. 49 Fersht A. The most influential journals: impact factor and eigenfactor[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(17): 6883-6884. 50 Thelwall M. Dimensions: a competitor to Scopus and the Web of Science?[J]. Journal of Informetrics, 2018, 12(2): 430-435. 51 Szomszor M, Adie E. Overton - A bibliometric database of policy document citations[OL]. (2022-01-19). https://arxiv.org/pdf/2201.07643.pdf. 52 Huang C Y, Dai H L. Learning from class-imbalanced data: review of data driven methods and algorithm driven methods[J]. Data Science in Finance and Economics, 2021, 1(1): 21-36. 53 Pozo R F, González A B R, Wilby M R, et al. Prediction of on-street parking level of service based on random undersampling decision trees[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 8327-8336. 54 Feng S, Keung J, Yu X, et al. Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction[J]. Information and Software Technology, 2021, 139: 106662. 55 Xin L K, Rashid N B A. Prediction of depression among women using random oversampling and random forest[C]// Proceedings of the 2021 International Conference of Women in Data Science at Taif University. IEEE, 2021: 1-5. 56 Batista G E A P A, Prati R C, Monard M C. A study of the behavior of several methods for balancing machine learning training data[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 20-29. 57 Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python[J]. The Journal of Machine Learning Research, 2011, 12: 2825-2830. |
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