From causes of conflict to solutions: Shifting the lens on human–carnivore coexistence research
Bibliographic record
Abstract
Abstract Human‐carnivore conflicts pose significant challenges in the management and conservation of carnivores across the globe. Abundant research has led to generalizable insights into the causes of such conflicts. For example, conflicts predictably occur when carnivores have access to human food resources, particularly when their natural foods are scarce. However, similar insights into the effectiveness of interventions aimed at coexistence remains comparatively scarce. We hypothesized that this disparity might be reflected in a bias toward research focused on causes of conflict rather than interventions to address it. To test our hypothesis, we evaluated the content of studies on human–carnivore conflicts and coexistence in Canada and the United States from 2010 to 2021. We found that studies disproportionately focused on causes of conflict, with that discrepancy increasing through our study period. We also found a disproportionate focus on black bears and wolves and western jurisdictions, and a disproportionate use of observational (vs. experimental) approaches. Studies on conflict interventions were primarily directed at the carnivores themselves (e.g., lethal approaches) versus human elements (e.g., attractant management, policies), despite evidence that the latter are more effective. We expect that a shift in focus toward solutions‐oriented research, integrating insights across geographies, taxa, social contexts, and disciplines, would facilitate effective interventions and foster coexistence, improving outcomes for people and carnivores alike.
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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 | 0.058 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.026 | 0.015 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".