List of Boxes. List of Tables and Figure. Preface. Abbreviations. Introduction. 1. Capitalism and the Capitalist Type of State. 2. The Keynesian Welfare National State. 3. The Schumpeterian Competition State. 4. Social Reproduction and the Workfare State. 5. The Political Economy of State Rescaling. 6. From Mixed Economy to Metagovernance. 7. Towards Schumpeterian Workfare Postnational Regimes?. Notes. References. Index.

The Future of the Capitalist State.

Neil Brenner has in the past few years made a major impact on the ways in which we understand the changing political geographies of the modern state Simultaneously analyzing the restructuring of urban governance and the transformation of national states under globalizing capitalism, 'New State Spaces' is a mature and sophisticated analysis of broad interdisciplinary interest, making this a highly significant contribution to the subject

New State Spaces: Urban Governance and the Rescaling of Statehood

State rescaling, policy experimentation and path dependency in post-Mao China: a dynamic analytical framework. Regional Studies. This paper evaluates the applicability of the state rescaling framework for framing politico-economic evolution in China. It then presents an analytical framework that examines institutional change as driven by the dynamic entwinement of state rescaling, place-specific policy experimentation and institutional path dependency. The framework problematizes simple ‘transition’ models that portray a mechanistic ‘upward’ or ‘downward’ reconfiguration of regulatory relations after market-like rule was instituted in 1978. It emphasizes, instead, a more established pattern of development marked simultaneously by geographically distinct (and enduring) institutional forms and experimental (and capricious) attempts to transcend them.

State rescaling, policy experimentation and path dependency in post-Mao China: a dynamic analytical framework

This article provides the introduction to a special issue on Green Economies in the Global South, that sheds light on the causes, complexities, consequences, and different practices of state engage...

Tracing Sustainability Transformations and Drivers of Green Economy Approaches in the Global South

Decoding national new area development in China: Toward new land development and politics

Recent studies have re-examined the implications and conceptual limitations of the theory of state rescaling in non-Western contexts. While the reconfiguration of state spaces has taken place in ma...

Scalar restructuring of the Chinese state: The subnational politics of development zones:

Low-resolution infrared-based human activity recognition (HAR) attracted enormous interests due to its low cost and private. In this article, a novel semi-supervised cross-domain neural network (SCDNN) based on $8\times8$ low-resolution infrared sensor is proposed for accurately identifying human activity despite changes in the environment at a low cost. The SCDNN consists of feature extractor, domain discriminator, and label classifier. In the feature extractor, the unlabeled and minimal labeled target domain data are trained for domain adaptation to achieve a mapping of the source domain and target domain data. The domain discriminator employs the unsupervised learning to migrate data from the source domain to the target domain. The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data. Experimental results show that the proposed method achieves 92.12% accuracy for recognition of activities in the target domain by migrating the source and target domains. The proposed approach adapts superior to cross-domain scenarios compared to the existing deep learning methods, and it provides a low cost yet highly adaptable solution for cross-domain scenarios.

Cunyi Yin

Papers

Scalar restructuring of the Chinese state: The subnational politics of development zones:

Human Activity Recognition With Low-Resolution Infrared Array Sensor Using Semi-Supervised Cross-Domain Neural Networks for Indoor Environment