Ecological agriculture and rural revitalization: toward a post-productivist countryside in Nanjing, China
Bibliographic record
Abstract
The Chinese central government has launched the Rural Revitalization plan as a basic state policy. This has led to the growth of ecological agriculture (EA) in rural China as an approach to achieve rural development. However, whether transitions to EA can facilitate rural reinvigoration is controversial both in China and in other countries. These rural transformations are of interest to scholars, policy makers, and the public. Drawing on the theoretical framings of post-productivism, this study focuses on two villages in Nanjing and their transitions to EA, one that embarks on green agritourism and one that cooperates in organic rice production. The finding is that EA has shifted full-scale transitions away from dominant productivism to different post-productivist ends, i.e., a consumption version of the post-productivist countryside and an agriculture-revitalized version of the post-productivist countryside. This study stresses the significance of using post-productivism to characterize heterogeneous rural changes but warns of the indiscriminate match of post-productivism to resolutions for rural dilemmas. Empirical findings suggest that spatial accommodations to the farming culture and peasant lifestyle are important for retaining a vigorous rural community. A major insight is that a socio-spatial lens is necessary to refine the conceptualization of post-productivism and to understand the depth of rural sustainability transitions. This study argues that greater rural revitalization can be achieved only if more nuanced socio-cultural adaptations to spatial restructuring are considered.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".