Econometric analysis of the causes of forest land use changes in Hainan, China
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the effects of economic, demographic, and institutional factors on land allocation between forestry and other uses. A panel data set from Hainan Island in China and a generalized least squares estimation method, allowing individual effects for counties, are applied. The results indicate that higher timber prices have led to an acceleration in rain forest exploitation, but encouraged investment in plantation forests. Population growth is the driving force behind the loss of natural forests, but it is positively related to plantation forests. Decollectivization seems to have promoted plantation forests, but has not saved the rain forest. A higher share of forestry land owned by state-owned enterprises also fosters afforestation on wasteland, but seems to lead to faster exploitation of natural forest, at least initially. The uncertainty that existed in the early period of economic reform quickened the pace of resource extraction and deterred investment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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) | 0.001 | 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 it