Sustainable rural renewal in China: archetypical patterns
Bibliographic record
Abstract
Against the backdrop of rural deprivation during the rapid urbanization of China since the end of the previous century, rural renewal has been regarded as a vital strategy for facilitating rural sustainability.Rural renewal in contemporary China involves activities that replan, consolidate, and redevelop the extant and idle rural construction land and then convert such land for alternative uses, including new rural settlement construction and rural industry development.However, given the regionally decentralized authoritarian (RDA) regime of China, i.e., a combination of political centralization and economic regional decentralization, the governance of rural renewal and its performance show great diversity.The objective of this study was to explore and elucidate the underlying patterns of sustainable rural renewal.Thus, from the social-ecological systems (SES) perspective, an archetype analysis was conducted based on primary data from 27 cases from the eastern, central, and western parts of China.In total, eight archetypical patterns were extracted, and the following three overarching implications were observed: (1) a governance system aligning with the attributes of rural land resources, the characteristics of actors, and the properties of interactions is essential for sustainable rural renewal; (2) decentralized or self-organized governance emerges to facilitate sustainable rural renewal; and (3) a long-term perspective of designing and enforcing rural renewal and distinctive land resource endowment contribute to rural sustainability.These findings may benefit China and other regions pursuing rural sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".