Spatial patterns of illegal resource extraction in Kibale National Park, Uganda
Bibliographic record
Abstract
SUMMARY Conservation policy typically excludes people from national parks and manages encroachment by law enforcement. However, local people continue to extract resources from protected areas by boundary encroachment and poaching. This paper quantifies the patterns of illegal resource extraction from Kibale National Park in Uganda, the demand for Park resources by communities bordering the Park, and examines whether designated resource access agreements reduce illegal extraction. Sections of the Park boundary were examined and human entry trails, wood extraction, livestock grazing, and animal poaching signs were quantified. Levels of illegal extraction were compared with the demand for and admitted illegal access to resources inside the Park, collected in a survey of households located near the Park. Extraction was also compared between villages with and without negotiated resources access agreements. The most wanted and extracted resource from the Park was wood for fuel and construction. Implementation of resource access agreements with local community associations was found to be an effective means of reducing illegal extraction, but only if the association members profited from the agreement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".