1Plenary 2: Governance, Resources and Co-management POLAR BEAR AS A MULTIPLE USE RESOURCE IN NUNAVUT: LOCAL GOVERNANCE AND COMMON PROPERTY CONFLICTS
Bibliographic record
Abstract
While promising to protect the bear and its ecosystem, Canada also recognized its responsibilities to its Inuit citizens and their cultural and socioeconomic needs (Lentfer 1974). The management of polar bears in Canada allows Inuit communities to hunt bears for subsistence and to outfit and guide polar bear sport hunts for non-Inuit. Throughout the twentieth century, polar bear hides, as a by-product of subsistence hunting, were sold in the fur trade and currently each fetches roughly $1.000. Outfitting and guiding a sport hunt provides up to 20 times more income to the community than the sale of a bear hide. Thus, Canada’s decision to allow sport hunting creates an important financial opportunity for Inuit. Despite the monetary benefits, it was only in the 1980s that polar bear sport hunts in Canada increased beyond 10 per year (out of some 400 animals harvested annually). At that time the European sealskin market ended, causing severe economic hardship to Inuit hunters, who, today, still continue to hunt seals for food. The sale of sealskins, as a by-product of Inuit food hunting, had supported the subsistence economy by providing money for the purchase of hunting supplies (see Wenzel 1991). The increase in polar bear sport hunts offered after the seal skin market crash suggest it was a deliberate effort by Inuit to offset monetary losses from the sealskin trade, rather than a desire to commoditize the polar bear harvest. In an attempt to improve economic conditions, the Canadian government promoted tourism in the North throughout the 1980s (Myers and Forrest 2000, Wenzel 2005). Despite this incentive, by the 1990s the majority of bear tags were still not assigned to the sport hunt. Even today, sport hunts in the case study communities discussed below make up only approximately 50 % of those community quotas. The lack of economic efficiency of the situation across Nunavut suggests that there is conflict between culture/subsistence hunting and the sport hunt industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.001 |
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".