The Study on Effects of Concurrent Business on Cultivated Land Use Efficiency -Based on Empirical Analysis of Gansu and Qinghai Province
Bibliographic record
Abstract
This paper uses the farmer household’s model to structure the analytical framework of concurrent business cultivated land use behavior and its efficiency and then uses group comparison analysis and data envelopment analysis (DEA) to analyze the diversity of cultivated land use behavior and efficiency, among different types of concurrent business farmer which is based on the promise of separation not established and investigation data of farmer household. The results demonstrate that the concurrent business types have a significant influence on utilization patterns and farmer household’s behavioral options, different types of farmers’ investment, management and land scale have a big difference. Generally speaking, capital and labor input of concurrent business farmer household are high than that of specialized farmer, concurrent business farmer household Class ? are higher than that of Class ?; and different utilization patterns lead to the difference of cultivated land use efficiency. Moreover, technical efficiency of specialized farmer household is higher than that of concurrent business farmer household Class ? based on the separation was not established, and both of them are higher than that of concurrent business farmer household Class ?. That illustrates land use efficiency of farmer household will reduce as the level of concurrent business increase.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".