Analyzing Agricultural Agglomeration in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has been little scholarly research on Chinese agriculture’s geographic pattern of agglomeration and its evolutionary mechanisms, which are essential to sustainable development in China. By calculating the barycenter coordinates, the Gini coefficient, spatial autocorrelation and specialization indices for 11 crops during 1981–2012, we analyze the evolutionary pattern and mechanisms of agricultural agglomeration. We argue that the degree of spatial concentration of Chinese planting has been gradually increasing and that regional specialization and diversification have progressively been strengthened. Furthermore, Chinese crop production is moving from the eastern provinces to the central and western provinces. This is in contrast to Chinese manufacturing growth which has continued to be concentrated in the coastal and southeastern regions. In Northeast China, the Sanjiang and Songnen plains have become agricultural clustering regions, and the earlier domination of aquaculture and rice production in Southeast China has gradually decreased. In summary, this paper provides a political economy framework for understanding the regionalization of Chinese agriculture, focusing on the interaction among the objectives, decisionmaking behavior, path dependencies and spatial effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it