Wildlife and spiritual knowledge at the edge of protected areas: raising another voice in conservation
Bibliographic record
Abstract
International guidelines recommend the integration of local communities within protected areas management as a means to improve conservation efforts. However, local management plans rarely consider communities knowledge about wildlife and their traditions to promote biodiversity conservation. In the Sebitoli area of Kibale National Park, Uganda, the contact of local communities with wildlife has been strictly limited at least since the establishment of the park in 1993. The park has not develop programs, outside of touristic sites, to promote local traditions, knowledge, and beliefs in order to link neighboring community members to nature. To investigate such links, we used a combination of semidirected interviews and participative observations (N= 31) with three communities. While human and wildlife territories are legally disjointed, results show that traditional wildlife and spiritual related knowledge trespasses them and the contact with nature is maintained though practice, culture, and imagination. More than 66% of the people we interviewed have wild animals as totems, and continue to use plants to medicate, cook, or build. Five spirits structure humanwildlife relationships at specific sacred sites. However, this knowledge varies as a function of the location of local communities and the sacred sites. A better integration of local wildlifefriendly knowledge into management plans may revive communities’ connectedness to nature, motivate conservation behaviors, and promote biodiversity conservation.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".