Humanitarian Demining and Sustainable Land Management in Post-Conflict Settings in Sri Lanka: Literature Review
Bibliographic record
Abstract
Systematic humanitarian demining carried out with care is an essential prerequisite for sustainable land management in post conflict settings. Degradation of land and pollution of water, soil and vegetation, as well as poisonous gas emissions that may even contribute to climate change, can be reduced significantly by humanitarian demining practices. Such practices simultaneously conserve natural resources and increase yields which results to sustainable land management. Mine Risk Education which is a major component of humanitarian demining, will have a lasting impact on people’s knowledge, attitudes and practices related to landmines making a positive contribution towards sustainable land management. This paper utilizes research publications from refereed journals and mine action authorities as well as ground information using the systematic literature review (SLR) method. The study investigates relations between humanitarian demining and sustainable land management in post conflict settings with a classic example from North East Sri Lanka. The practical implications for demining operators are that they can implement the strategies to improve the prevailing sustainable land management conditions of the communities in Sri Lanka and elsewhere.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".