Estimation of the Tourism Benefits of Kakamega Forest, Kenya: A Travel Cost Approach
Bibliographic record
Abstract
Policymakers in developing countries emphasize on short-term benefits of forest conversion over the long-term economic benefits such as recreational uses. This study used travel cost method to estimate the potential tourism or recreational benefits of two segments of Kakamega forest which are managed differently by Kenya Wildlife Services (KWS) and Kenya Forest Service (KFS). The empirical data used for this study was collected from past records on tourists’ numbers and their country of origin from both management stations of the forest. The travel cost approach was then used to analyze the data. Results show that the forest generates considerable economic benefits, thereby justifying its continued conservation as a destination for tourists from Europe, USA and other parts of the world. Further, the results indicate that areas which are better conserved and protected (managed by KWS) have higher recreational benefits than those that are not well conserved (managed by KFS). This paper discusses Kakamega forest as an important tourism recreational site and justifies an increment in entrance fee for tourists.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".