Defence Against the Three Bears- Local Innovations fro Protection of Life and Property on Western Hudson Bay.pptx
Bibliographic record
Abstract
The relationship between humans and bears is changing in Canada’s Arctic and sub-Arctic. Churchill, Manitoba’s community members have co-existed among the polar bears for generations, locals are adapting and innovating as the social-ecological system is changing with the return of the barren-land grizzly bears to the landscape. While formal grizzly bear management plans are being developed at the provincial and federal levels, they have historically weighted Western knowledge above Local and Indigenous knowledge. This novel ecosystem is exposing the opportunity to continue fostering community-based research, development of grizzly bear specific safety protocols, and mitigation strategies for future human–grizzly bear conflict. A mixed method approach of semi-structured interviews and Q methodology, allows us to explore the human dimensions of wildlife tolerance and the future of human–bear co-existence on western Hudson Bay. I have found that there are species-specific patterns in how bears interact with property and humans out on the land . The human-bear relationship has already started changing and the community knows it. Residents want resources to deter and handle grizzly bear interactions, to mitigate risk, and for their voices to be valued.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".