Wild About Wolves: Using collaboration and innovation to bridge parks, people, and predators
Bibliographic record
Abstract
Abstract Human‐carnivore conflicts present an array of conservation challenges, especially in complex and cross‐cultural settings. Described here is a facilitated, multi‐method, collaborative process in the Nuu‐chah‐nulth First Nations' Traditional Territory, British Columbia, Canada, aimed at building a project to address human‐wolf conflicts following the species' natural re‐colonization of a national park reserve. Participants reported that this project prompted dialogue and engagement that will help bridge the gap between First Nations and non‐Indigenous people in the Territory. Although the project remains ongoing, pragmatic lessons about its process can already be identified: (1) an early, and ongoing collaboration was crucial in setting the project's priorities; (2) adopting a co‐learning approach set a respectful tone for the project; and (3) reframing human‐wolf conflicts using a tolerance‐oriented lens bridged diverse perspectives and worldviews. The preliminary outcomes of these efforts to date are constitutively different from conventional collaborative efforts because the process has already changed relationships in ways that many such previous efforts have not.
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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".