Examining the links between livelihood sustainability and environmental protection in the anti-poverty relocation and settlement program areas: An empirical analysis of Shaanxi, China
Bibliographic record
Abstract
Consistent with the 2030 agenda for sustainable development, China has initiated a large-scale anti-poverty relocation and settlement program (the ARSP), aiming to restore ecosystems and lift impoverished populations out of the poverty trap and into sustainable livelihoods. Unlike previous studies that focus on the population issues of the ARSP, we examine the links between livelihood sustainability and environmental protection (“livelihoods–environment”) in the ARSP areas. We found that the links are generally weak, with low levels of both livelihood sustainability and environmental protection. The disorder category is the most common in both the overall and the regional samples, with the mild and borderline disorder categories being the most common subcategories. The results varied regionally, and indicated that environmental problems can be more prominent in regions with fragile environments. Household-level distribution shows significant differences in the strengths of the links among different demographic groups, and regression results show that higher levels of average education, loan amount, and asset holdings, as well as lower proportions of the elderly and non-agricultural activities were associated with stronger links. These household factors influenced the links through different mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".