Kananaskis country’s road to coexistence: exploring expert perceptions of roadside bear viewing and management strategies
Bibliographic record
Abstract
In North America, bear viewing is becoming increasingly popular with visitors to parks and protected areas. In the face of heightened visitation pressures in parks, the phenomena of roadside bear viewing poses risks to humans and wildlife. A related challenge is the formation of “bear jams,” which is traffic congestion caused by people stopping or slowing down to view bears. Using Peter Lougheed Provincial Park (PLPP) in Kananaskis, Alberta, as a case study, we examined the gaps in our understanding of roadside bear viewing from a human dimensions approach. To gain insight into management strategies, risks, and observed human behavior associated with roadside bear viewing, semi-structured interviews (n=22) were conducted with expert participants, including park staff members, non-profit organization employees, and biologists. Responses emphasized the need for consistent messaging and better communication regarding respectful roadside bear viewing behaviors, and recommendations for specific forms and methods of communication. Results of this study indicate that a holistic and adaptive approach could mitigate roadside bear viewing risks while also balancing conservation and recreation goals. Among the key contributions of this study is its insight into roadside bear management and viewing from a social sciences and human dimensions perspective
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".