National environmental programs and local social-ecological system change in dryland China: implications for environmental governance
Bibliographic record
Abstract
Interdependence of ecological and social systems is widely acknowledged, but consideration of how local communities are influenced by processes at other sectors or scales is often omitted. This research addresses this gap by examining the implementation of China’s national environmental programs (NEPs) to combat desertification. We investigate (a) the changes in local social-ecological systems and the role of the NEPs therein since the year 2000; (b) how the changes have affected local livelihoods and behaviors and attitudes toward the NEPs and the land; and (c) the role of other drivers in the changes and their implications. Interviews and surveys were conducted with scientists, grassroots implementers, and local farmers and herders. Secondary socioeconomic data were used to understand broader changes and drivers. Our results indicate that the NEPs generated both positive and negative biophysical and socioeconomic changes, and that they were both supported and disrupted by institutions at other sectors and scales. Although farmers and herders appreciated an improved environment and living standards, they suffered from other changes, such as reduced arable land area, rising costs of living and production, precarious markets, and extreme weather events. Absence of social security and limited social capital made farmers and herders unable to engage in long-term practices that support land conservation and their well-being. The findings highlight the need to foster systemic resilience in local communities through the provision of social security and social capital building to navigate the changing world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".