Chasing baboons or attending class: protected areas and childhood education in Uganda
Bibliographic record
Abstract
SUMMARY The influence of protected areas on childhood education is often assumed to be positive, and integrated conservation and development programmes (ICDPs) typically support childhood education by building schools, providing scholarships and improving education quality, which in turn helps build conservation attitudes. In this paper, the impact of a protected area on childhood education is examined within the broader socioeconomic context of villages bordering Kibale National Park (Uganda). Survey data from households and primary schools indicated ICDPs improved primary school enrolment and education for girls. However, crop raiding by Park-protected animals reduced the probability of boys completing four years of primary education because they were preferentially held back from school to guard crops. Since population growth around protected areas is a threat to conservation, and since extending education for both boys and girls helps reduce birth rates and improve future employment opportunities, helping children attain primary school completion supports both conservation and development objectives. The findings highlight the need to continue supporting childhood education near protected areas; however, additional focus should be placed on boys’ educational attainment, and the need for wildlife authorities, governments and conservation organizations to invest in crop-raiding defences to mitigate crop-raiding losses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".