Broadening the spectrum of conflict and coexistence: A case study example of human-wolf interactions in British Columbia, Canada
Bibliographic record
Abstract
Coexistence has seen an explosive rise within conservation social science scholarship. While this represents an exciting shift in the field, many academics are still skeptical. Some scholars have expressed concerns around the omission of "conflict", naïveté, and impracticality associated with coexistence literature. In this paper, we aim to demonstrate that critiques of coexistence often stem from reductionism and decontextualization, process inefficiencies and/or inequities, failure to address and prioritize human well-being as a goal, and a lack of tools to foster open, collaborative dialogue. We draw on a case study of human-wolf interactions in the Pacific Rim National Park Reserve Region, British Columbia, Canada, to illustrate how coexistence efforts can, and should, prioritize "conflict", be attentive to the real challenges of sharing spaces with wildlife, and encourage collaborative, inclusive processes that work toward tangible, actionable outcomes. We conducted 32 semi-structured interviews with residents from diverse backgrounds and levels of experience with wolves in the region. From these interviews, we articulated novel, co-developed, contextual definitions of human-wolf conflict and coexistence in the region. We then developed a collaborative tool for visualizing behavioral and cognitive elements of human-wildlife interactions through open and inclusive dialogue, using real examples from these research interviews. The research findings highlight three main principles: (1) that conflict and coexistence are contextual and should be understood as such, (2) that coexistence requires collaborative processes that pay attention to equity and inclusivity, and (3) that there are frameworks or tools that can help facilitate discussions toward practical outcomes of coexistence projects. We believe that this paper helps to disambiguate coexistence and reinforce that coexistence requires focused attention to the well-being of people as much as wildlife.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".