Sustainable grain production growth of farmland–A role of agricultural socialized services
Bibliographic record
Abstract
The main aim of this study is to examine the evolving landscape of agricultural socialized services and their impact on the consistent growth of grain production in China. Using panel data from 2007 to 2020, we employ the Entropy Method to gauge the dynamic changes in agricultural socialized services that have contributed to the steady increase in grain production. The research methods include static panel, mediator, and threshold regression models to investigate the effects and mechanisms underpinning the improvement of agricultural socialized services on grain production growth. The empirical findings demonstrate a significantly positive correlation between enhanced agricultural socialized services, such as means of production services, sci-tech information services, and social public services, and increased grain production. This positive impact persists even with limited grain production resources. A mediating effect was identified, whereby agricultural socialized services indirectly stimulate grain production growth by encouraging large-scale agricultural land management. Furthermore, threshold analysis indicates the presence of a single threshold effect linked to the level of agricultural socialization services. This threshold effect plays a pivotal role in the relationship between large-scale agricultural management and steady grain production growth. The study suggests an enhancement of agricultural socialized services can promote sustained growth in grain production.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".