Demand and proximity: drivers of illegal forest resource extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Illegal extraction from protected areas is often shaped by the surrounding socio-economic landscape. We coupled village-scale socio-economic parameters collected using household surveys with measured levels of illegal resource extraction proximate to study villages to investigate the socio-economic drivers of illegal extraction from Kibale National Park, Uganda. The level of illegal tree harvesting and the number of illegal entry trails into the Park were driven by subsistence demand from villages adjacent to the Park and by for-profit extraction to supply local urban markets, whereas grazing in the Park was linked to high livestock ownership. Capital asset wealth, excluding livestock, was found to mitigate illegal resource extraction from the Park. We also found high human population density to coincide spatially with park-based tourism, research and carbon sequestration employment opportunities. Conservation strategies should be integrated with national policy to meet the needs of local communities and to manage urban demand to reduce illegal extraction from protected areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it