Situation and Property Rights in Agricultural and Unused Lands Upland of Vietnam
Bibliographic record
Abstract
"The research aims at identifying the status of the management and use of agricultural and unused land in the upland areas of Central Vietnam via a case study of Hong Bac commune to identify issues relating to the exploitation and land use. This research is based on the bundles of property rights analysis framework and on field investigations through site surveys and discussions among the groups who manage and use the land, including: the State (commune and district), community and households. The analysed research results have demonstrated the \nperformance as well as activities of the property right bundles to the local people and the State as for agricultural land and unused land; have identified and classified the existing formal and informal rights relating to the two kinds of land in the survey location; have clarified the reasons of the existence as well as the impact of the rights to the land exploitation of the local people. The \nresearch has evaluated the status and changes of land in general and agricultural land and unused land in particular, from 2000 to 2008. It has also analysed the reasons for the changes of agricultural and unused land and of crop structure. The reasons are the changes in the land policies of the State, the spontaneous changes in crops and land exploitation of the people for earning their livelihood due to the general economic changes of the district and the demands of the agricultural product market."
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".