Tourism in Kenya's national parks: A cost-benefit analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
East Africa is home to some of the most stunning wildlife in the world. With tourism in the region’s wildlife parks growing in popularity, it is imperative to evaluate the socioeconomic and environmental costs and benefits of this expanding industry. This study conducted a cost-benefit analysis of the various impacts that tourism has brought to Kenya’s national parks by monetarily valuating each impact. While the results of this cost-benefit analysis suggest that the benefits far outweigh the costs, even when non-measurable costs are considered, a number of fundamental issues must be addressed in order to improve the cost-benefit balance. The results are likely to be representative of the overall state of tourism in Kenya’s national parks and expose key areas where improvements can be made. Improvements to tourism in Kenya’s national parks can have positive implications for local people, the environment, wildlife species, tourists, and biodiversity conservation. Keywords: tourism; national parks; Kenya; cost-benefit analysis
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it