Mine smartness and the community voice in mine-risk education: lessons from Afghanistan and Angola
Bibliographic record
Abstract
Abstract Mine-risk education programmes will fall short of their intended impact for as long as they fail to take into account local responses—knowledge, logic and everyday practices—to mine threats. Community information, systematically collected through household and institutional surveys, can help to define and understand endogenous ‘mine smartness’. The same evidence provides insight into the impact of mine-risk education, including its unintended consequences. Using six criteria of mine smartness, ciet carried out evaluations of mine-risk education in Afghanistan (1997) and Angola (1999). The first clear lesson to be drawn from these evaluations is that people in mine-affected areas do generate their own broadly effective means of facing the daily threat of mines. The second lesson is that people take risks for reasons that make sense to them: ‘education’ that landmines are dangerous probably adds little value for them. The third lesson is that mine-risk education that does not take into account these first two lessons can cause harm. The evaluations produced evidence of unintended risk-taking by people exposed to mine-risk education programmes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".