Based on the Third-party Perspective of Collective Forest Tenure Reform in Hunan Province
Bibliographic record
Abstract
The paper dealt with the main doings of collective forest right system reform in Hunan province through surveys such as on-the-spot survey,interviews,personnel introduction,and individual conversation of individual site.Major problems of collective forest right system reform were analyzed.The paper also introduces the effectiveness of Hunan forestry reform and the characteristics of its reform.And then it analyses the collective forestry right system reform in Hunan,and points out that the particularity of the existing main problems.Learning from the doings of Jiangxi,Fujian,Guangdong,Guangxi as outstanding pilot experience,and drawing lessons from successful experience of world forestry powers such as the United States,Japan,Germany,Canada,and New Zealand,the paper puts forward counter measures and suggestions to promoting reform to improve the forest right system reform and the complete service system.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".