Tsá7ts7acw aylh ta Nkyápa muta Míxalha (Coyote and Bear in shared happiness): Salish-Bear entanglements, transformations and collective stewardship <sup/>
Bibliographic record
Abstract
Based on long-term collaborative ethnographic partnership with Indigenous Interior Salish Upper St’át’ímc Elders in the Fraser River region of today's British Columbia, this collaborative paper contextualises a particular Nk̓yáp (Coyote) transformer story and the communal role of Bear(s) in place, time and within a complex kin-based practice of caring for the land. Frequently, this story is employed to educate on trickstery, control, disenchantment and negative reciprocity. Simultaneously, it informs about positive reciprocity, astonishment, respectful, practical and moral conduct in times of radical social and environmental transformation. It highlights a particular St’át’ímc ethos of care and law of the land that humans and non-humans now employ to continuously recreate a ‘land of plenty’ toward a good life and to reclaim areas on a territorial basis also pre-empted by colonial, capitalist and industrial institutions. This particular law of the land is Tśíl in St’át’ímcets , or happiness. Following a key protagonist – Bear – through the story and into land use planning and collective stewardship, we argue for Bear and humans as collaborative stewards of the environment following principles of mutual respect, reciprocity, reverence and responsibility. We present a key comparative lesson for collaborative research, interspecies understandings and enduring entanglements toward the generative politics of storytelling and stewardship relations within an inclusive community-of-life and toward living well.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".