Border Crossings and Polar Bears: How Indigenous Hunting Rights in Canada Become Part of a Transnational Economy
Bibliographic record
Abstract
This article considers a specific, highly complex, and contentious case study, the uniquely Canadian phenomenon of the sale of hunting rights by First Nations Canadians to non-Indigenous, non-Canadian trophy hunters who want to hunt polar bears in Canada. These hunters, largely of European ancestry, come mainly from the United States and, more recently, from Western Europe as well. Ultimately, this analysis demonstrates both the necessity and the utility of anchoring transnational analyses in a more-than-human world because access to and protection of living non-human beings plays a crucial role in defining nations and communities. The essay addresses questions such as “What can we understand about the role of the Canadian state, the national government, the US-Canadian border, the philosophical constitution of a more-than-human world in both Indigenous and European-derived epistemologies, and the politics of Indigeneity in the international marketplace, through this one case study focused on human-animal relations?”
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".