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Purpose
This paper aims to explore the relationship between ethical self-fashioning and citizenship practices in the ongoing revival of "Chinese Traditional Culture" pursued in tandem by the party-state and by private actors in present-day China.


Design/methodology/approach
Adopting an anthropological approach, the author draws from three sets of resources: (1) research literature on China's political history and key texts of early Chinese thought, (2) contemporary state discourses on citizen formation, and (3) participant observation notes and interviews with organizers and followers of the Wu-Wei School (a pseudonym). The author conducts a textual analysis of primary and secondary literature and a critical discourse analysis of the ethnographic data and examines emerging themes.


Findings
Firstly, the author identifies a crucial dimension in the historical and cultural roots of Chinese citizenship practices: an enduring conception that binds individual ethical self-improvement with socio-political flourishing. Secondly, examining contemporary state discourses on "citizen quality" and "reviving China's outstanding traditional culture", the author showcases how party-state authorities call on individuals to self-reform for national rejuvenation. Thirdly, the author investigates how members of the Wu-Wei School construe their individual pursuits of ethical self-improvement as significant for societal progress.


Originality/value
Based on these findings, the author demonstrates the ways in which autochthonous conceptions of Chinese citizenship give a central place to private acts of self-fashioning. The author argues that the entanglement between individual ethics and citizenship practices constitutes a crucial but largely understudied dimension of Chinese citizenship.

Precise and timely information on crop spatial distribution over large areas is paramount to agricultural monitoring, food security, and policy development. Currently, automatically classifying crop types at a large scale is challenging due to the scarcity of ground data. Although previous studies have indicated that transductive transfer learning (TTL) is a promising method to address this problem, it performs poorly within regions where crop compositions and phenology differ largely. Here we transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest of the study area where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. The final crop maps had a high accuracy with overall accuracy generally greater than 0.89, with user's accuracy and producer's accuracy ranging from 0.72 to 0.98. Moreover, the resulting maps were consistent with the NUTS-2 level official statistics, with R2 consistently greater than 0.9. We further analyzed the crop rotation patterns and found that the rotation intervals across these EU countries were generally at least one year. Maize was dominantly rotated with winter Triticeae crops or converted to other land covers in the following year. Rapeseed was generally grown in rotation with winter Triticeae crops, whereas the rotation patterns of winter and spring Triticeae crops were more diversified. Red Edge Position (REP) and Normalized Difference Yellow Index (NDYI) played significant roles in crop classification across the EU. This study highlights the potential of the developed TTL method for crop classification over large spatial extents where labeled data are limited and the differences in crop compositions and phenology are relatively large.