Fruit booms and investor mobility along the China-Myanmar and China-Laos borders
Bibliographic record
Abstract
Investment in fruit cultivation is currently transforming agricultural production and rural landscapes in the mountainous region of mainland Southeast Asia, especially in the borderlands and lowlands of this region. Unlike large-scale land acquisitions and investment in previously reported boom crops, e.g., rubber trees and oil palms, investment in fruit cultivation is generally short-term, small-scale, and often informal. Additionally, different from previous crop booms, investors in fruit booms often relocate geographically or spatially to seize opportunities. Research has yet to investigate this aspect of today’s investment boom in fruit cultivation. Beyond discussing a certain fruit type in a specific area, this study documents the geographic mobility of investment as the distinguishing characteristic of investment in fruit cultivation in Dehong, Xishuangbanna, Mandalay, and Luang Namtha, all of which are located along the China-Myanmar (Burma) and China-Laos borders. This is achieved through grounded methodological approaches. These sites have become a hot spot of booms in the production of fresh fruit, e.g., banana and watermelon. This investment mobility can be generally divided into the following two types: domestic investors relocating within one country, and foreign investors relocating across borders, thus, (re)locating investment. Comparison and synthesis are employed to show that ecological and social-political constraints drive investor mobility in fruit booms along liberalized agri-trade and regional comparative advantages. This study advances the understanding of associated issues by characterizing and excavating the geographic mobility of investors in the current era of small-scale land acquisitions and investment in fruit booms in a broader scope. These findings expand the theoretical literature on land grabbing and crop booms and help to (re)consider related environmental consequences and well-being of the affected population.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".