Providing health care to improve community perceptions of protected areas
Bibliographic record
Abstract
Impoverished communities often turn to illegal extraction of resources from protected areas to alleviate economic pressures or to make monetary gains. Such practices can cause ecological damage and threaten animal populations. These communities also often face a high disease burden and typically do not have access to affordable health care. Here we argue that these two seemingly separate challenges may have a common solution. In particular, providing health care to communities adjacent to protected areas may be an efficient and effective way to reduce the disease burden while also improving local perceptions about protected areas, potentially reducing illegal extraction. We present a case study of a health centre on the edge of Kibale National Park, Uganda. The centre has provided care to c. 7,200 people since 2008 and its outreach programme extends to c. 4,500 schoolchildren each year. Contrasting the provision of health care to other means of improving community perceptions of protected areas suggests that health clinics have potential as a conservation tool in some situations and should be considered in future efforts to manage protected areas.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".