Impact of Social Network Embedding on Cooperative Members’ Wage Income Distribution Mechanism and Access to Employment Information and Computational Modeling Analysis
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Bibliographic record
Abstract
Social network is a special social factor in the development of cooperatives, and the influence of the degree of social network embeddedness cannot be ignored in order to realize the high-quality development of cooperative economy.In this paper, we first use the entropy power method to measure and characterize the social network embeddedness, and then use the OLS regression model to analyze the influence mechanism of social relationship network embeddedness on the mechanism of wage income distribution and access to employment information of cooperative members, and explore the moderating role of environmental dynamics.The experimental results show that there is a certain strength gap in the external relationships of the social network of rural cooperative members, and the level of social relationship network embeddedness among samples from different regions is polarized.At the same time, the internal and external embeddedness of the social network of cooperative members has a positive effect on the efficiency of employment information acquisition, and there is a mediating role of the wage income distribution mechanism between the two.In addition, environmental dynamics moderates the two paths of action between social relationship network embeddedness and wage income distribution mechanism and employment information acquisition efficiency, but the moderating effect of environmental dynamics on capital income distribution mechanism and employment information acquisition efficiency is not significant.This study has certain guiding significance for the innovative development of cooperative economy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it