More bears, less bears: Inuit and scientific perceptions of polar bear populations on the west coast of Hudson Bay
Bibliographic record
Abstract
Inuit and scientific perceptions of polar bear populations are grounded in different epistemologies, relationships and interactions with polar bears. In many communities, the presence of polar bear hunting quotas has led to both external and internal conflicts. Inuit throughout Nunavut are seeing more polar bears in close proximity to their communities. Scientists argue that the increase in bear-human encounters is due to rapid environmental change, leading to a decrease, rather than an increase, in polar bear numbers. These opposing perceptions result in confrontation regarding the hunting and protection of bears. Within communities, ethical, social and economic conflicts arise with regard to the enactment of the quota system due to differing views on the allocation of the quota, the existence of a sport hunt, and the morality of such an intense focus on polar bears. One such community is Arviat, on the west coast of Hudson Bay. The people of Arviat feel particularly vulnerable to the year-round presence of polar bears in and near their community. The inclusion of women in the hunt and the cost of undertaking a bear hunt, lead to discussion and mixed feelings about the open hunting season each year. While the situation in Arviat is in some ways unique, it also serves as an example of the questions and concerns facing Inuit across the Canadian Arctic as they increasingly have to deal with polar bears on their doorstep.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".