Challenges of Community-Based Management of Protected Areas: Contested Trade-offs Between Livelihood and Conservation Interests in Chyulu Hills National Park, Kenya
Bibliographic record
Abstract
This was a formative study whose main aim was to obtain useful information for designing co-management interventions of Chyulu Hills National Park (CHNP) and other protected ecosystems in Kenya. Among the specific objectives that we have covered in this article were to; (1) examine the competing livelihood interests of communities living within the catchment of CHNP. (2) to assess the challenges that undermined community support towards co-management of the ecosystem. The findings showed that communities were highly dependent on park resources for both direct and indirect livelihood services. This over-dependence on the resources by local communities made it difficult for management agencies to control access to the protected area leading to illegal encroachment. There were gaps that required urgent attention to to ensure suatainable management of CHNP. First, communities were not engaged effectively when mangement decisions were being made but only received information on decisions that have already been made elsewhere and were required to abide by them. Secondly, incidences of human-wildlife conflicts jeopardized cooperation between communities and wildlife management agency. Thirdly, in spite of the protected area being in their neighbourhoods, community members felt entitled to the ecosystem resources and therefore they perceived it unfair that the authories kept them off from the National park. In conclusion, there was no balanced tradeoff between the livelihood interests of local communities and conservation interests of the National Park. To promote collaboration between the communities and wildlife management agencies in management of the park there was an urgent need to address the livelihood interests of the local communities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".