Luna and the Departed Ancestor: A Case Study of Animal Embodiment, Non-violent Resistance, and Indigenous Rights in Canada
Bibliographic record
Abstract
In 2004 the Canadian Government evoked the Marine Mammal Regulations of Canada to attempt capture of Luna, a juvenile orca whale in Nootka Sound, British Colombia. The Government argued that the relocation of the orca was necessary for both the animal's survival and for the health of the community. For members of the Mowachaht/ Muchalaht First Nation, however, capturing Luna would constitute an unlawful attempt to remove a group member from their native waters and signify an outright infringement on indigenous group rights. On the expected day of capture, conflict ensued as representatives of the Canadian Government were met on open water by members of the Mowachaht/ Muchalaht First Nation in their traditional canoes. After four days, the Canadian Government withdrew from Nootka Sound, while Luna remained free to roam the waters within the Mowachaht/ Muchalaht First Nation. This paper explores the politics of this conflict, and demonstrates how indigenous identity politics and displays of tradition enabled the Mowachaht/ Muchalaht First Nation to resist efforts by the Canadian Government to undermine Indigenous rights in Canada. My research ultimately concludes by arguing for reform to the Marine Mammal Regulations of Canada to better address the specific needs, wants, and desires of Indigenous communities in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.056 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".