Social media community groups support proactive mitigation of human-carnivore conflict in the wildland-urban interface
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding human reactions to potentially dangerous wildlife at the wildland-urban interface is central to mitigating human-wildlife conflicts. Social media is increasingly used to share information about wildlife among residents living in the interface. We used an online survey distributed on community Facebook groups in Victoria Beach, Manitoba — an area experiencing increasing wildlife sightings — to examine whether posts to the groups resulted in people using avoidance tactics to reduce human-wildlife interactions or conflicts. The results indicate that the majority of respondents used Facebook posts to change their behavior to avoid potential encounters with black bears, wolves, and coyotes. Despite few respondents having wildlife safety training, most respondents taught their children wildlife safety. Most respondents would not phone the local conservation authority, for reasons including distrust and concerns about lethal control. Coexistence attitudes towards wildlife management were dominant and respondents recognized the importance of protecting wildlife in the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it