Conservation through connection: Approaches to engaging communities in applied grizzly bear research
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
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.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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