Local Governments' Behaviors Study on the Improvement for Farmers' Cleaner Production in Factor Markets 1 ETUDE DU COMPORTEMENT DES GOUVERNEMENTS LOCAUX SUR LA PRODUCTION PROPRE DES PAYSANS DANS LES MARCHÉS DE FACTEUR
Bibliographic record
Abstract
Both in developed and developing countries, governments of all levels pay much attention to the weak industry----agriculture and try their best to find out the effective way to develop it. Cleaner production in agriculture is the effective way to realize sustainable development in China. According to the behavioral features of local governments and the actuality of agriculture development, the thesis holds that the realization of agricultural cleaner production depends on the interest games between economic bodies and perfect market system. Besides, set up the operation mechanism for agricultural products and factor markets will help the development of cleaner production. Local governments allocate the resources by controlling all kinds of markets. The thesis mainly discusses the local governments' behavior models based on factor markets. Adjust the farmers' production behaviors with the constraints of capital, lands, labor and so on. Optimize combination between multi-items. Local governments will influence farmers' choice by changing their production functions. The thesis also analyzes how can local governments accelerate the process of agricultural cleaner production and improve competitive power in local areas by influencing the management environment of agricultural products' producers.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".