Gugwilx'ya'ansk and goats: Indigenous perspectives on governance, stewardship and relationality in mountain goat (mati) hunting in Gitga'at territory
Bibliographic record
Abstract
Abstract Indigenous peoples' deep time relationships with ecosystems hold valuable lessons on how humans can relate to, and be stewards in, the natural world. At the crux of these lessons is the multifaceted way Indigenous peoples participate within ecosystems. This paper describes this multifaceted connection between people and place by analysing a legal and pedagogical philosophy called gugwilx'ya'ansk amongst the Ts'msyen (Tsimshian) people of the northwest coast of North America. The author, an Indigenous anthropologist from the Gitk’a’ata (Gitga'at) tribe of the Tsimshian, narrates how gugwilx’ya’ansk weaves education, governance, identity, spirituality, and ritual into land‐based practices for the purpose of deep‐time stewardship. Through autoethnographic narrative and storytelling, he focuses on his own journey of being groomed into becoming a mountain goat hunter within the hereditary governance system of his community, and how this process revealed a methodology to achieve relationality and reciprocity on the landscape while harvesting. This paper concludes by reflecting on why this Indigenous methodology has been successful for the author, and what lessons it has to offer greater society. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".