Local Experts’ Observations, Interpretations, and Responses to Human-Polar Bear Interactions in Churchill, Manitoba
Bibliographic record
Abstract
Since interactions and conflicts between polar bears (Ursus maritimus) and people are reportedly increasing across the Arctic, there is a pressing need to better understand how such conflicts can be prevented or their outcomes ameliorated. A great deal of knowledge about what strategies work for both preventing and mitigating human-polar bear conflicts lies with local experts, yet this knowledge has often remained relatively inaccessible to contemporary wildlife managers. This study had three main aims: to document and synthesize local knowledge of polar bear behaviour in Churchill, Manitoba, to characterize perceptions and interpretations of polar bears, and to examine the linkage between local experts’ knowledge, perceptions, and actions. We identified a suite of bear behaviours that local experts consistently observe and interpret as cues to the bears’ intent. These behaviours are not unique to this locale. Nevertheless, differences in perspectives on the predictability of polar bear behaviour and in interpretations of the nature of bears significantly influence study participants’ strategies for responding to bears. Our findings demonstrate that human-related factors are more complex than current models of human-bear interactions account for, so there is a need to develop richer models for understanding what motivates and influences human behaviours and responses towards bears.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".