The Risk of Living With Bears on Western Hudson Bay
Bibliographic record
Abstract
Social-ecological systems in Canada’s Arctic and sub-Arctic are changing. Although community members in Churchill, Manitoba have long co-existed with polar bears, increasing interactions with grizzly bears are complicating the human understanding of the human-bear relationship. This novel ecosystem is identified by the return of the barren-land grizzly bear population to the province and is exposes the need for adaptation and innovation to combat human-grizzly bear conflicts. I explore the relationships that people in Churchill have with the three bear species found locally (polar bears Ursus maritimus, black bears Ursus americanus, and grizzly bears Ursus arctos), focusing on local knowledge of the three bear species and how individuals’ familiarity with these species influences risk perceptions for coexisting. This research also explored what locals identified as current gaps and/ or limitations to the current bear management institutions to address the increase in grizzly bear presence in northern Manitoba. Data were collected by combining semi-structured interviews and Q methodology in a mixed methods approach. I found that local perceptions of risk and bear species-specific knowledge have been influenced by generational knowledge, the geography of land activities, previous educational training, interaction experiences, and more. I found a total of four unique perspectives emerged based on the theme of species-specific knowledge, as well as three distinct perspectives on the theme of risk. Locals indicated that they possess limited options and knowledge to protect their property and themselves from grizzly bears. They are extremely interested in participating and supporting future grizzly bear research efforts and I have outlined recommendations for researchers and wildlife managers on what is needed to ameliorate human-wildlife conflicts, gain community support for conservation plans, and be adaptive to the evolving social-ecological system on western Hudson Bay. Overall, this thesis provides insights on the human dimensions of a novel ecosystem and how frameworks like the adaptive cycle of innovation can be used to guide policy makers, wildlife managers, and resources to support human-wildlife coexistence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".