Factors on Spatial Heterogeneity of the Grain Production Capacity in the Major Grain Sales Area in Southeast China: Evidence from 530 Counties in Guangdong Province
Bibliographic record
Abstract
Grain security is an essential issue for countries across the world. China has witnessed over the last decades not only a rapid growth in the volume of the grain production, but also a divergence in its geographical distributions. Existing studies on the influencing factors of grain production have overlooked thus spatial heterogeneity. This paper investigates the factors that cause the geographical heterogeneity in grain output levels in Guangdong province of China, in terms of land, labor and capital. To address the spatial attenuation effect of the influencing factors, we use the Geographically Weighted Regression (GWR) on samples of different spatial ranges, which include a total of 530 southern counties from 2015 to 2017. The results show that (a) the effect of land endowment on grain output vary across the east and the west, and between coastal and inland areas; (b) the effect of labor endowment on grain output are inconsistent in the sign and magnitude of the estimates across counties; (c) the effect of agricultural capital on grain production shows heterogeneity spatially (across the east and the west) and economically (across developed and less developed regions). We then analyze the potential mechanism behind this spatial heterogeneity, as well as its policy implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".