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| Core Element Identification and Governance Structure Evolution of Policies: A Policy Citation Perspective |
| Ba Zhichao1,2, Fan Chenglei1,2, Liu Leilei1,2, Li Gang3 |
1.Research Institute for Data Management & Innovation, Nanjing University, Suzhou 215163
2.School of Information Management, Nanjing University, Nanjing 210023
3.Center for Studies of Information Resources, Wuhan University, Wuhan 430072 |
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Abstract Identifying the core elements and structure of a policy system facilitates tracking the evolution of governance concepts and disclosing paradigms of policy formulation and design; this helps promote the reuse and migration of policy elements. This study leverages policy content mining and citation network analysis techniques to understand the evolving trajectory and governance structure of core policy objectives, instruments, and measures within a specific sector. First, a template library encapsulating the core elements of policies is developed, enabling the identification and extraction of contextual elements and citation relationships within the policy system through machine-learning algorithms. Next, a refined PageRank algorithm optimized for node jump probability is introduced to identify core elements across different periods and construct a time-series citation network by incorporating the inherent attributes and citation features of policy documents. Finally, an analysis of the dynamic ranking and evolving network structure of subcategories of policy objectives, instruments, and measures sheds light on the developmental pathways, structural configuration characteristics, and governance patterns of the core elements. Utilizing China’s large-scale energy policies from 1980 to 2020 as an empirical case, the results reveal that an optimized PageRank algorithm incorporating policy attributes provides a more precise evaluation of the actual influence and significance of policy elements. Furthermore, energy policy objectives exhibit a multi-tiered and diversified evolutionary structure, with a preferential focus on environmental policy tools, followed by demand-oriented and supply-oriented policy tools. Additionally, policy governance methods are evolving toward greater completeness and rationality, with policy changes exhibiting a compatible and incremental evolutionary trajectory.
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Received: 12 July 2024
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1 胡吉明. 政策文本研究: 从内容计算到功能理解[J]. 图书情报知识, 2023, 40(4): 145-152.
2 Lasswell H D. The emerging conception of the policy sciences[J]. Policy Sciences, 1970, 1(1): 3-14.
3 张维冲, 王芳, 赵洪. 基于全要素网络构建的大规模政策知识关联聚合研究[J]. 情报学报, 2023, 42(3): 289-303.
4 周毅, 陈必坤, 马江华, 等. 基于文本量化分析的我国公共数据治理政策发展研究[J]. 情报学报, 2023, 42(4): 436-452.
5 李新, 李柏洲, 吴翔宇. 创新型城市中府际关系与政策工具的社会网络[J]. 科学学研究, 2020, 38(12): 2258-2270.
6 Huang C, Yang C, Su J. Identifying core policy instruments based on structural holes: a case study of China’s nuclear energy policy[J]. Journal of Informetrics, 2021, 15(2): 101145.
7 Kotilainen K, Aalto P, Valta J, et al. From path dependence to policy mixes for Nordic electric mobility: lessons for accelerating future transport transitions[J]. Policy Sciences, 2019, 52(4): 573-600.
8 Zhang C, Guan J C. How policies emerge and interact with each other? A bibliometric analysis of policies in China[J]. Science and Public Policy, 2022, 49(3): 441-459.
9 张剑, 黄萃, 叶选挺, 等. 中国公共政策扩散的文献量化研究——以科技成果转化政策为例[J]. 中国软科学, 2016(2): 145-155.
10 Ba Z C, Ma Y X, Cai J Y, et al. A citation-based research framework for exploring policy diffusion: evidence from China’s new energy policies[J]. Technological Forecasting and Social Change, 2023, 188: 122273.
11 黄萃, 苏竣, 施丽萍, 等. 政策工具视角的中国风能政策文本量化研究[J]. 科学学研究, 2011, 29(6): 876-882, 889.
12 张永安, 周怡园. 新能源汽车补贴政策工具挖掘及量化评价[J]. 中国人口·资源与环境, 2017, 27(10): 188-197.
13 Yang C, Huang C. Exploring the diversity and consistency of China’s information technology policy[J]. Journal of Information Science, 2024, 50(6): 1605-1628.
14 俞露露, 张洪艳, 胡广伟. 政策工具视角下我国智慧社区政策特征分析与审思[J]. 情报科学, 2024, 42(2): 24-34, 55.
15 霍帆帆, 霍朝光, 马海群. 我国数据治理相关政策量化剖析: 发展脉络、政策主体、政策渊源与政策工具[J]. 情报学报, 2023, 42(12): 1424-1437.
16 Yang C, Huang C. Quantitative mapping of the evolution of AI policy distribution, targets and focuses over three decades in China[J]. Technological Forecasting and Social Change, 2022, 174: 121188.
17 Yang C, Huang C, Su J. A bibliometrics-based research framework for exploring policy evolution: a case study of China’s information technology policies[J]. Technological Forecasting and Social Change, 2020, 157: 120116.
18 胡吉明, 阳巧英. “目标-工具”框架下我国档案利用服务政策演进分析[J]. 档案学通讯, 2024(1): 61-69.
19 Yan S, Pan L Z, Lu Y, et al. Towards sustainable drug supply in China: a bibliometric analysis of drug reform policies[J]. Sustainability, 2023, 15(13): 10040.
20 Bouma J A, Verbraak M, Dietz F, et al. Policy mix: mess or merit?[J]. Journal of Environmental Economics and Policy, 2019, 8(1): 32-47.
21 赵蓉英, 吴胜男. 基于引证关系的知识转移的理论研究[J]. 情报理论与实践, 2014, 37(12): 28-32.
22 Ba Z C, Zhao Y X, Liu X T, et al. Spatio-temporal dynamics and determinants of new energy policy diffusion in China: a policy citation approach[J]. Journal of Cleaner Production, 2022, 376: 134270.
23 Ba Z C, Tang Y, Liu X T, et al. Tracing policy diffusion: identifying main paths in policy citation networks[J]. Journal of Information Science, 2023. DOI: 10.1177/01655515231189660.
24 Freeman L C. Centrality in social networks conceptual clarification[J]. Social Networks, 1978-1979, 1(3): 215-239.
25 Freeman L C. A set of measures of centrality based on betweenness[J]. Sociometry, 1977, 40(1): 35-41.
26 Bonacich P. Factoring and weighting approaches to status scores and clique identification[J]. The Journal of Mathematical Sociology, 1972, 2(1): 113-120.
27 Brin S, Page L. The anatomy of a large-scale hypertextual web search engine[J]. Computer Networks and ISDN Systems, 1998, 30(1-7): 107-117.
28 陈伟, 林超然, 孔令凯, 等. 基于专利文献挖掘的关键共性技术识别研究[J]. 情报理论与实践, 2020, 43(2): 92-99.
29 曹琨, 吴新年, 白光祖, 等. 基于“科学-技术”复杂网络的关键核心技术识别研究——以数控机床领域为例[J]. 数据分析与知识发现, 2025, 9(3): 42-55.
30 顾洁, 胡安安, 刘旭, 等. 社交网络正、负影响力计算——基于符号网络的PageRank算法改进[J]. 情报学报, 2015, 34(7): 725-733.
31 Ding Y, Yan E J, Frazho A, et al. PageRank for ranking authors in co-citation networks[J]. Journal of the American Society for Information Science and Technology, 2009, 60(11): 2229-2243.
32 王雪莹, 刘珊, 陈洪侃, 等. 中美知识融合网络与知识扩散网络对比[J]. 情报杂志, 2024, 43(5): 167-175, 166.
33 Yan E J, Ding Y. Discovering author impact: a PageRank perspective[J]. Information Processing & Management, 2011, 47(1): 125-134.
34 段庆锋, 朱东华, 汪雪锋. 基于改进PageRank算法的引文文献排序方法[J]. 情报理论与实践, 2012, 35(1): 115-119.
35 陈兰杰, 赵元晨. 政策工具视角下我国开放政府数据政策文本分析[J]. 情报资料工作, 2020, 41(6): 46-53.
36 Zhang G X, Gao Y, Li J X, et al. Author Correction: China’s environmental policy intensity for 1978-2019[J]. Scientific Data, 2022, 9: Article No.181.
37 郑新曼, 董瑜. 基于科技政策文本的程度词典构建研究[J]. 数据分析与知识发现, 2021, 5(10): 81-93.
38 张国兴, 高秀林, 汪应洛, 等. 中国节能减排政策的测量、协同与演变——基于1978—2013年政策数据的研究[J]. 中国人口·资源与环境, 2014, 24(12): 62-73.
39 张国兴, 张振华, 管欣, 等. 我国节能减排政策的措施与目标协同有效吗?——基于1052条节能减排政策的研究[J]. 管理科学学报, 2017, 20(3): 162-182.
40 Kong Y, Feng C, Yang J. How does China manage its energy market? A perspective of policy evolution[J]. Energy Policy, 2020, 147: 111898.
41 Ortiz D, Leal V. Energy policy concerns, objectives and indicators: a review towards a framework for effectiveness assessment[J]. Energies, 2020, 13(24): 6533.
42 王洛忠, 张艺君. 我国新能源汽车产业政策协同问题研究——基于结构、过程与内容的三维框架[J]. 中国行政管理, 2017(3): 101-107.
43 Rothwell R, Zegveld W. Reindustrialization and technology[M]. London: Longman, 1985.
44 Zhang H M, Xu Z D, Sun C W, et al. Targeted poverty alleviation using photovoltaic power: review of Chinese policies[J]. Energy Policy, 2018, 120: 550-558.
45 郭本海, 李军强, 张笑腾. 政策协同对政策效力的影响——基于227项中国光伏产业政策的实证研究[J]. 科学学研究, 2018, 36(5): 790-799.
46 彭纪生, 仲为国, 孙文祥. 政策测量、政策协同演变与经济绩效: 基于创新政策的实证研究[J]. 管理世界, 2008(9): 25-36.
47 Murphy L, Meijer F, Visscher H. A qualitative evaluation of policy instruments used to improve energy performance of existing private dwellings in the Netherlands[J]. Energy Policy, 2012, 45: 459-468.
48 Wang H Q, Zhao T Y, Cooper S Y, et al. Effective policy mixes in entrepreneurial ecosystems: a configurational analysis in China[J]. Small Business Economics, 2023, 60(4): 1509-1542.
49 Huang C, Su J, Xie X, et al. A bibliometric study of China’s science and technology policies: 1949-2010[J]. Scientometrics, 2015, 102(2): 1521-1539.
50 Yang C, Huang C. Target-oriented policy diffusion analysis: a case study of China’s information technology policy[J]. Scientometrics, 2024, 129(3): 1347-1376.
51 Huang C, Yang C, Su J. Policy change analysis based on “policy target-policy instrument” patterns: a case study of China’s nuclear energy policy[J]. Scientometrics, 2018, 117(2): 1081-1114.
52 Xiang J Q, Ma F C. Government agencies and their roles in the diffusion of intellectual property policy in China: analysis based on a policy literature reference network[J]. International Review of Administrative Sciences, 2021, 87(4): 888-907.
53 Spearman C. The proof and measurement of association between two things[J]. The American Journal of Psychology, 1904, 15(1): 72-101.
54 West J, Bergstrom T, Bergstrom C T. Big macs and eigenfactor scores: don’t let correlation coefficients fool you[J]. Journal of the American Society for Information Science and Technology, 2010, 61(9): 1800-1807. |
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