Towards a new paradigm: the development of China's forestry in the 21 <sup>st</sup> century
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
SUMMARY China's forestry has been changing drastically since the country was affected by devastating floods in 1998. The government has launched a series of key national programmes and forest policy reforms. The scale and investment of these forestry programmes are already producing some tangible benefits to forest cover, the wood industry and rural livelihoods. Large areas are protected from logging, huge afforestation programmes are underway, and ongoing privatization offers hope of more efficient and effective operations that can create jobs and stimulate economic growth. However, the changes have been associated with many problems, particularly for rural communities that can no longer harvest wood and have now been displaced. This paper examines current patterns in forest development and the impact of this on the environment and economic and rural development.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 it