Category, Characteristics and Influence Mechanism of Farmers’ Cooperatives in China——Based on the Analysis of 8 Typical Case
Bibliographic record
Abstract
Since the beginning of this century, farmers' cooperatives have been developing rapidly in China. Based on the principles of classic cooperatives, this paper investigates the farmers' cooperatives in practice in China. It is found that the cooperatives in practice do not conform to the principles of classic cooperation, and have strong characteristics based on realistic conditions. In practice, from the perspective of industrial chain, China's farmer cooperatives can be divided into four categories: engaged in production, engaged in "production + sales", engaged in "production + processing + sales" and providing services, and there are significant differences in the operation mode and governance structure of all kinds of cooperatives. For the reasons behind it, the author investigates the concentration degree of different types of cooperatives in the operation of various agricultural products, and found that different category of products have different demands on the functions of cooperatives, which makes the operation mode and governance structure of cooperatives formed for different functional needs present diversified characteristics. In addition, the author also points out that although there are essential differences between the farmers' cooperatives in practice and those in the classical, they can still play their unique functions, that is to say, they can influence the identity and income of farmers by changing the utilization of production elements in the traditional agricultural management mode.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".