Landmines and Local Community Adaptation
Bibliographic record
Abstract
Despite international mobilization for greater humanitarian mine action and despite considerable clearance achievements, the majority of mine‐affected communities have not yet been involved in formal clearance activities. They adapt to the contamination largely by local means. The differing degree to which local adaptation is successful is now better understood as a result of the Global Landmine Survey, a multi‐country survey project launched in the wake of the 1997 Ottawa treaty to ban anti‐personnel mines. Socio‐economic impact surveys have since been completed in several countries. In addition to landmines, the Global Landmine Survey records impacts also from unexploded ordnance (UXO). The ability to avoid mine incidents is used to measure adaptation success. We use a variant of Poisson regression models in order to identify community and contamination correlates of the number of recent landmine victims. We estimate separate models using data from the Yemen, Chad and Thailand surveys. We interpret them in a common framework that includes variables from three domains: Pressure on resources, intensity of past conflict and communities’ institutional endowments. Statistically significant associations occur in all three domains and in all the three countries studied. Physical correlates are the most strongly associated, pointing to a lasting deadly legacy of violent conflict, but also significant learning effects over time are present. Despite different measurements of institutional endowments, in each country one factor signifying greater local development is correlated with reductions in victims, whereas factors commonly associated with the presence of government officials do not contribute to local capacity to diminish the landmine problem. Strong spatial effects are manifest in clusters of communities with recent victims. Two policy consequences emerge. Firstly, given humanitarian funding limits, trade‐offs between clearing contaminated land and creating alternative employment away from that land need to be studied more deeply; the Global Landmine Survey will need to reach out to other bodies of knowledge in development. Secondly, communities with similar contamination types and levels often form local clusters that are smaller than the administrative districts of the government and encourage tailored planning approaches for mine action. These call for novel coalitions that bring advocacy and grassroots NGOs together with local governments, agricultural and forestry departments and professional mine clearance and awareness education agencies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".