Stakeholder Perceptions of Success in Human-Carnivore Coexistence Interventions
Bibliographic record
Abstract
Human-carnivore coexistence (HCC) on agricultural lands affects wildlife and human communities around the world, whereby a lack of HCC is a central concern for conservation and farmer livelihoods alike. For intervention strategies aimed at facilitating HCC to achieve their desired goals it is essential to understand how interventions and their success are perceived by different stakeholders. Using a grounded theory approach, interviews (n=31) were conducted with key stakeholders (commercial livestock farmers, conservationists and protected area managers) involved in HCC scenarios in Limpopo, South Africa. Interviews explored perceptions of successful intervention strategies (aimed at increasing HCC), factors that contribute to perceptions of strategy effectiveness and whether coexistence was a concept that stakeholders considered achievable. The use of grounded theory emphasised the individual nature and previously unexplored facets to HCC experiences. The majority of stakeholders based their measures of success on changes in livestock loss. Concern has been raised over the subjectivity and reliance on recall that this measure involves, potentially reducing its reliability as an indicator of functional effectiveness. However, it was relied on heavily by users of HCC interventions in our study and is therefore likely influential in subsequent behaviour and decision-making regarding the intervention. Nonetheless, perceptions of success were not just shaped by livestock loss but influenced by various social, cultural, economic and political factors emphasising the challenges of defining and achieving HCC goals. Perceptions of coexistence varied; some stakeholders considered farmer-carnivore coexistence to be impossible, but most indicated it was feasible with certain caveats. An important element of inter-stakeholder misunderstanding became apparent, especially regarding the respective perceptions of coexistence and responsibility for its achievement. Without fully understanding these perceptions and their underpinning factors, interventions may be restricted in their capacity to meet the expectations of all interested parties. The study highlights the need to understand and explore the perceptions of all stakeholders when implementing intervention strategies in order to properly define and evaluate the achievement of HCC goals.
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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 | 0.027 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".