Who welcomes the bear: Evidence for a disconnect between attitudes and acceptability of killing brown bears
Bibliographic record
Abstract
Abstract Acceptability of large carnivores is influenced by socio‐psychological factors and is crucial to coexistence. When large carnivores cause real or perceived threats such as damage to local economy or safety, people may engage in lethal control. However, in the presence of legal protection for the species and associated penalties, lethal retaliation can diminish or happen elusively. Therefore, it is helpful for managers to understand people's attitudes and acceptability associated with support for killing large carnivores in conflict situations and the demographics of those involved in lethal retaliation. We interviewed 390 respondents living in 26 villages in northern Iran, where communities are largely dependent on agriculture and livestock for their livelihood and conflicts with brown bears ( Ursus arctos ) are common. Our goal was to assess the acceptability of killing bears in four different scenarios, from low‐intensity to high‐intensity interaction. The results showed that although respondents generally had a slightly positive attitude towards bears, those with negative attitudes were associated with higher acceptability of killing bears. The mean acceptability of killing bears increased as human–bear interaction intensified. Younger, less educated and female respondents were more supportive of killing bears, while respondents with an alternative source of income were less accepting of killing them. We provide five recommendations to foster coexistence, including leveraging positive attitudes through strategies like building advocacy networks, promoting inclusive outreach programmes, particularly for female and younger respondents. Also, we recommend emergency conflict mitigation teams take immediate action for conflict mitigation in areas with higher acceptance of killing bears to prevent retaliatory behaviour. Furthermore, providing an alternative source of income and focusing on preventive methods and effective strategies are recommended. Read the free Plain Language Summary for this article on the Journal blog.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".