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Titlebook: Official Governance and Self-governance; The Reconstruction o Qingzhi Zhou Book 2022 Social Sciences Academic Press 2022 Chinese villages.S

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发表于 2025-3-21 19:41:54 | 显示全部楼层 |阅读模式
书目名称Official Governance and Self-governance
副标题The Reconstruction o
编辑Qingzhi Zhou
视频video
概述Explores the complexities of Chinese governance on the ground.Explores the way that the Chinese government obtains legitimacy from local stakeholders.Offers a framework to understand issues of China‘s
图书封面Titlebook: Official Governance and Self-governance; The Reconstruction o Qingzhi Zhou Book 2022 Social Sciences Academic Press 2022 Chinese villages.S
描述.This book offers a new interpretation of the evolution and modernization of social order in China’s grass-roots communities. Traditionally, social order at the grass-roots level was maintained through an organic combination of self-rule by the people and authority rule by political leaders. As a hybrid form of social order, therefore, it not only has features of autonomy but also reflects the power of the state apparatus. Despite the modernization of the nation-state, the old rules sustaining social order at the grass-roots level are still very much in effect and have seamlessly integrated into the new social structure. Unless we fully appreciate this fundamental continuity, we would not be able to understand how social order at the grass-roots level today is upheld and functions. And the modernization of social order at the grass-roots level is most fruitfully studied from the perspectives of the construction of modern public social relations and the development of grass-roots systems of social autonomy..
出版日期Book 2022
关键词Chinese villages; Sustainable development; Urban-rural divide; Local government in China; Agriculture an
版次1
doihttps://doi.org/10.1007/978-981-19-6601-9
isbn_softcover978-981-19-6603-3
isbn_ebook978-981-19-6601-9
copyrightSocial Sciences Academic Press 2022
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978-981-19-6603-3Social Sciences Academic Press 2022
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Qingzhi Zhouwhich acts as a filtering step before enumeration. We provide a detailed analysis on the time complexity of the existing nested NER techniques and conduct extensive experiments on two datasets. The results demonstrate that BOUNCE outperforms various nested NER techniques and achieves higher efficien
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rating model to weighted sum the hidden node embeddings encoded by these GCNs into the final node embeddings. We also conduct a disparity constraint on these hidden node embeddings when training to ensure that non-redundant information is captured from different features. Experimental results on fiv
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