Attitudes towards the Sri Lankan leopard <i>Panthera pardus kotiya</i> in two rural communities
Bibliographic record
Abstract
Abstract Livestock depredation by wild carnivores threatens carnivore populations and livestock-dependent human communities globally. Understanding local attitudes towards carnivores can inform strategies to improve coexistence. In Sri Lanka, the dairy industry is expanding, creating a need for proactive conflict mitigation. Livestock depredation by the Endangered Sri Lankan leopard Panthera pardus kotiya occurs, but little is known about these incidents or the attitudes of those whose livelihoods may be threatened by this. We surveyed people in two rural communities, Palatupana and Maskeliya, that differed in the scale of livestock ownership, livestock management practices and socio-ecological factors, to characterize attitudes towards leopards and understand their determinants. In Palatupana, an area with extensive cattle rearing, depredation incidents were frequent, and attitudes towards leopards were positively related to respondents' age, number of dependants, years spent rearing livestock and a greater overall support for wildlife conservation. Attitudes were negatively related to respondents' knowledge of leopard ecology and awareness of leopard-related tourism, from which cattle owners do not benefit. In Maskeliya, where cattle rearing is secondary to other agricultural work, depredation did not occur. Here, attitudes were positively related to a desire for increased government assistance with cattle rearing. The inability to develop land for cattle husbandry was a common barrier experienced in both communities. Considering local attitudes can inform strategies to improve human–carnivore coexistence. Approaches that could improve attitudes towards leopards include involvement of cattle owners in tourism programmes, exploring potential alternative land ownership schemes, and improving infrastructure and access to veterinary care.
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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 | 0.000 |
| 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".