Benefit-sharing from protected area tourism: A 15-year review of the Rwanda tourism revenue sharing programme
Bibliographic record
Abstract
The success of protected areas depends to a large degree on the support of local communities living in and around these areas. Research has shown that where communities receive tangible and/or intangible benefits, from protected areas they are often more supportive of conservation. Rwanda introduced a tourism revenue sharing policy in 2005 to ensure that local communities receive tangible benefits specifically from protected area tourism and to enhance trust between the Rwanda Development Board (the then Rwanda Office of Tourism and National Parks) and local communities, and to incentivize the conservation of wildlife and protected areas. This study reviewed the tourism revenue sharing programme over the last 15 years, including primary and secondary data, which included interviewing more than 300 community members living around three national parks, as well as other relevant stakeholders. The results show that the tourism revenue sharing programme has resulted in a positive linkage between the national parks and development. Since 2005, ~80% of the funding was used for infrastructure and education projects. The funds are distributed through local community cooperatives, and most local people who are members of these cooperatives had received or were aware of tangible benefits received by the community and tended to have more positive attitudes toward tourism and the national parks. Despite a large amount of tourism revenue being disbursed over the 15-year period, there are still challenges with the programme and the overall impact could be enhanced. Recommendations as to how to address these are presented.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".