Attitudes towards carnivores: the views of emerging commercial farmers in Namibia
Bibliographic record
Abstract
Abstract The emerging commercial farmers in Namibia represent a new category of farmer that has entered the freehold farming sector since Namibia's independence in 1990. Several assessments of agricultural training needs have been carried out with these farmers but the issue of human–carnivore conflict has not yet been addressed. This study investigated one of the key components driving human–carnivore conflict, namely the attitudes of these farmers towards carnivores and how this affects the level of conflict and carnivore removal. We observed that the attitudes of these farmers are similar to farmers elsewhere. In general, farmers reported high levels of human–carnivore conflict. Many farmers perceived that they had a carnivore problem when sighting a carnivore or its tracks, even in the absence of verified carnivore depredation. Such sightings were a powerful incentive to prompt farmers to want to take action by removing carnivores, often believed to be the only way to resolve human–carnivore conflict. Nonetheless, our study showed that farmers who understood that carnivores play an ecological role had a more favourable attitude and were less likely to want all carnivores removed. We found that negative attitudes towards carnivores and loss of livestock, especially of small stock, predicted actual levels of human–carnivore conflict. Goat losses additionally predicted actual carnivore removals. We discuss the implications of our findings in relation to the activities of support structures for emerging commercial farmers in Namibia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".