Conservation through connection: Approaches to engaging communities in applied grizzly bear research
Bibliographic record
Abstract
Human-wildlife dynamics is a growing field and one of considerable importance to conservation. Wild spaces are in short supply, and consequently wildlife and people increasingly share the landscape, though not necessarily by choice. As a result, peoples’ needs might not be prioritized over those of wildlife, even in cases of human-wildlife conflict. For wildlife conservation to be effective and human-wildlife coexistence possible, the needs of both wildlife and people must be simultaneously addressed. Rather than an afterthought or a sentence in the conservation/management implications section of a paper, community engagement should be addressed before, during, and after a research project. However, this can be a difficult and often complicated task, for multiple reasons. Building relationships founded on trust, respect and reciprocity with community members takes commitment, time, skill, and a willingness by researchers to be open-minded in terms of methodologies and new ideas. Different cultural norms, beliefs, perspectives and biases can further exacerbate these challenges. Here, we share three short case studies reflecting our own research experiences engaging with communities in the field of grizzly bear (Ursus arctos) ecology and conservation science. We conclude with guidelines for advancing effective community engagement and suggestions for tackling some common barriers. Overall, we offer considerations for a practical and more holistic approach to large carnivore conservation, established on a foundation of strong community support.
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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 | 0.096 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.057 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.009 | 0.038 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".