Pastoralism and ecosystem‐based adaptation in Kenyan Masailand
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess the potential for pastoral communities inhabiting Kenyan Masailand to adapt to climate change using conservancies and payments for ecosystem services. Design/methodology/approach Multiple methods and data sources were used, comprising: a socio‐economic survey of 295 households; informal interviews with pastoralists, conservancy managers, and tourism investors; focus group discussions; a stakeholder workshop. Monthly rainfall data was used to analyse drought frequency and intensity. A framework of the interactions between pastoralists' drought coping and risk mitigation strategies and the conservancy effects was developed, and used to qualitatively assess some interactions across the three study sites. Changes in household livestock holdings and sources of cash income are calculated in relation to the 2008‐09 drought. Findings The frequency and intensity of droughts are increasing but are localised across the three study sites. The proportion of households with per capita livestock holdings below the 4.5 TLU poverty vulnerability threshold increased by 34 per cent in Kitengela and 5 per cent in the Mara site, mainly due to the drought in 2008‐2009. Payment for ecosystem services was found to buffer households from fluctuating livestock income, but also generates synergies and/or trade‐offs depending on land use restrictions. Originality/value The contribution of conservancies to drought coping and risk mitigation strategies of pastoralists is analyzed as a basis for evaluating the potential for ecosystem‐based adaptation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".