Technical efficiency analysis of the conversion of cropland to forestland program in Jiangxi, Shaanxi, and Sichuan
Bibliographic record
Abstract
Conversion of Cropland to Forestland Program (CCFP) has greatly impacted China’s agricultural sector, and more specifically rural farmers. While changes in living standards as a result of the implementation of the CCFP have been analyzed, little research has been conducted regarding the impacts of such policies on farming operations. As agriculture contributes nearly 10% of national GDP, it is important to analyze the implications of policies on a national industry. An input-oriented data envelopment analysis (DEA) model was used to examine the technical efficiency of farming operations following implementation of the CCFP, using survey data from farmers in Jiangxi, Shaanxi, and Sichuan provinces. Additionally, the impact of factors such as urbanization, age and education, and land fragmentation was examined with respect to farming operational efficiency. Scale inefficiency was found to have the greatest effect on overall inefficiency in farming operations in comparison to pure technical inefficiency, which was largely influenced by the presence and degree of land fragmentation of land holdings. Findings can be used to inform national land-use policies facilitating land fragmentation in China and address gaps in existing broader level studies that utilize non-parametric approaches to examine the technical efficiency of Chinese farmers affected by the CCFP.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".