Why keep lions instead of livestock? Assessing wildlife tourism‐based payment for ecosystem services involving herders in the <scp>M</scp>aasai <scp>M</scp>ara, <scp>K</scp>enya
Bibliographic record
Abstract
Abstract This paper examines the effects of wildlife tourism‐based payments for ecosystem services (PES) on poverty, wealth inequality and the livelihoods of herders in the Maasai Mara Ecosystem in south‐western Kenya. It uses the case of Olare Orok Conservancy PES programme in which pastoral landowners have agreed to voluntary resettlement and exclusion of livestock grazing from their sub‐divided lands. These lands are set aside for wildlife tourism, in return for direct monetary payments by a coalition of five commercial tourism operators. Results show that, on the positive side, PES is the most equitable income source that promotes income diversification and buffers households from the livestock income declines during periods of severe drought, such as in 2008‐2009. Without accounting for the opportunity costs, the magnitude of the PES cash transfer to households is, on average, sufficient to close the poverty gap. The co‐benefits of PES implementation include the creation of employment opportunities in the conservancy and provision of social services. There is however a need to mitigate the negative effects of PES, including the widening inequality in income between PES and non‐PES households and the leakages resulting from the displacement of settlements and livestock to currently un‐subdivided pastoral commons.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".