Community perception of the real impacts of human–wildlife conflict in Laikipia, Kenya: capturing the relative significance of high-frequency, low-severity events
Bibliographic record
Abstract
Abstract Biodiversity conservation outside protected areas requires cooperation from affected communities, hence the extensive discussions of trade-offs in conservation, and of a so-called new conservation that addresses human relations with nature more fully. Human–wildlife conflict is one aspect of those relations, and as land use intensifies around protected areas the need to understand and manage its effects will only increase. Research on human–wildlife conflict often focuses on individual species but given that protecting wildlife requires protecting habitat, assessments of human–wildlife conflict should include subsidiary impacts that are associated with ecosystem conditions. Using a case study from Laikipia, Kenya, where conservation outside protected areas is critical, we analysed human–wildlife conflict from a household perspective, exploring the full range of impacts experienced by community members on Makurian Group Ranch. We addressed questions about four themes: (1) the relationship between experienced and reported human–wildlife conflict; (2) the results of a high-resolution assessment of experienced human–wildlife conflict; (3) the relative impact of high-frequency, low-severity conflict vs high-severity, low-frequency conflict; and (4) the effect of experienced conflict on receptivity to the conservation narrative. Our results show that high-frequency, low-severity conflict, which is often absent from reports and discussion in the literature, is a significant factor in shaping a community's perception of the cost–benefit ratio of conservation. Local, ongoing, high-resolution monitoring of human–wildlife conflict may facilitate more realistic and effective incorporation of the experienced impacts of human–wildlife conflict in conservation planning and management. Such monitoring could help to define locally appropriate trade-offs in conservation and thereby improve conservation outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".