Local attitudes toward Apennine brown bears: Insights for conservation issues
Bibliographic record
Abstract
Human‐carnivore coexistence is a multi‐faceted issue that requires an understanding of the diverse attitudes and perspectives of the communities living with large carnivores. To inform initiatives that encourage behaviors in line with conservation goals, we focused on assessing the two components of attitudes (i.e., feelings and beliefs), as well as norms of local communities coexisting with Apennine brown bears ( Ursus arctos marsicanus ) for a long time. This bear population is under serious extinction risks due to its persistently small population size, which is currently confined to the long‐established protected area of Abruzzo, Lazio and Molise National Park (PNALM) and its surrounding region in central Italy. We interviewed 1,611 residents in the PNALM to determine attitudes and values toward bears. We found that support for the bear's legal protection was widespread throughout the area, though beliefs about the benefits of conserving bears varied across geographic administrative districts. Our results showed that residents across our study areas liked bears. At the same time, areas that received more benefits from tourism were more strongly associated with positive feelings toward bears. Such findings provide useful information to improve communication efforts of conservation authorities with local communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".