Reducing Environmental Damage Caused by the Collection of Cooking Fuel by Refugees
Bibliographic record
Abstract
I The collection of fuelwood by large numbers of internally displaced people and refugees for the purpose of providing energy for food preparation and cooking can cause environmental devastation and adversely affect the socio-economic balance with local populations. There is no simple solution. Reducing environmental impact, and thus easing societal tensions, requires addressing a complex set of issues including supply of and demand for natural resources, aid agency operations, willingness to utilize refugee knowledge and experience, the effects of forced displacement, poverty, and lack of land. The key to establishing sustainable solutions, whether fuel or non-fuel alternatives, requires being able to identify and understand the interaction between human needs and behaviour and the local environment. This paper explores the scope of the problem and offers case examples, describes efforts taken and alternatives available, presents outcomes of evaluations that have been performed, and outlines lessons learned to be used in future crises.abstract
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".