Evaluating Attitudes towards Large Carnivores within the Great Bear Rainforest
Bibliographic record
Abstract
Cohabitation between humans and carnivores is vital to the continued existence and integrity of ecosystems, often playing a large role in the success of large carnivore conservation. We focus on interactions between humans and carnivores in the world’s largest, relatively intact temperate rainforest—The Great Bear Rainforest (GBR), British Columbia, Canada. Specifically, we focus on residents of Prince Rupert, a city within the GBR, and examine its residents’ ecological and relational attitudes towards the surrounding area of protected rainforest and the large carnivores present in the area. We aim to determine the strength of public attitudes and values of the environment and carnivores in the GBR, and to examine whether they differ between First Nations and non-First Nations residents of Prince Rupert, British Columbia. We conducted 28 semi-structured interviews of Prince Rupert residents. At the start of the interview, respondents self-administered a survey consisting of statements from the Social Ecological Relational Value and the New Ecological Paradigm scales. We find no significant difference between First Nations and non-First Nations respondent attitudes. This is possibly due to three factors: (1) cultural influence from the local First Nations, (2) the fact that these carnivores are important for the local economy through tourism, and (3) a strong sense of place associated with the area and the carnivores that inhabit it regardless of positive or negative encounters with these animals. While we find positive attitudes towards carnivores and little evidence of human–wildlife conflict, feelings towards carnivores encountered in town or while hiking tend to be negative, especially when they involve wolves. In order to mitigate these effects in a way that protects these valuable creatures, respondents overwhelmingly clamored for a conservation officer to be assigned to Prince Rupert. We conclude that policy and management might alleviate human–carnivore conflicts in the area should our results be corroborated by studies with larger sample sizes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".