Applying a Systematic Review to Land Use Land Cover Change in Northern Upland <scp>V</scp>ietnam: The Missing Case of the Borderlands
Bibliographic record
Abstract
Abstract As V ietnam embraces the market economy, and a number of state policies promote reforestation and rural market integration, land use and land cover ( LULC ) changes are occurring in the country's northern uplands in increasingly complex and fragmented ways. Yet understandings of the degree and consequences of LULC changes in this diverse agro‐ecological region are incomplete. We conduct a systematic literature review of research reported in academic articles tracing and analysing LULC change in V ietnam's northern regions. We find that these studies have tended to take place away from the most mountainous, northern borderlands. The studies nonetheless highlight a diversity of land use land cover changes caused by numerous causes, making the distinction of overall trends difficult. To complement and extend this body of research, we introduce recent LULC change research we have completed in the mountainous border districts of L ào C ai province, on the S ino‐ V ietnamese border. The heterogeneity of causes of LULC change in both the review articles and our case study points to the importance of adapting land use policies to local agro‐ecological and socio‐economic conditions and ethnic diversity, taking into account state–farmer relations, household livelihood decision‐making, and policy implementation at the commune and district levels.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".