Lessons about parks and poverty from a decade of forest loss and economic growth around Kibale National Park, Uganda
Bibliographic record
Abstract
We use field data linked to satellite image analysis to examine the relationship between biodiversity loss, deforestation, and poverty around Kibale National Park (KNP) in western Uganda, 1996-2006. Over this decade, KNP generally maintained forest cover, tree species, and primate populations, whereas neighboring communal forest patches were reduced by half and showed substantial declines in tree species and primate populations. However, a bad decade for forest outside the park proved a prosperous one for most local residents. Panel data for 252 households show substantial improvement in welfare indicators (e.g., safer water, more durable roof material), with the greatest increases found among those with highest initial assets. A combination of regression analysis and matching estimators shows that although the poor tend to be located on the park perimeter, proximity to the park has no measureable effect on growth of productive assets. The risk for land loss among the poor was inversely correlated with proximity to the park, initial farm size, and decline in adjacent communal forests. We conclude the current disproportionate presence of poor households at the edge of the park does not signal that the park is a poverty trap. Rather, Kibale appears to provide protection against desperation sales and farm loss among those most vulnerable.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".