“When the Wild Roses Bloom”: Indigenous Knowledge and Environmental Change in Northwestern North America
Bibliographic record
Abstract
Indigenous Peoples in Northwestern North America have always worked with predictable cycles of day and night, tides, moon phases, seasons, and species growth and reproduction, including such phenological indicators as the blooming of flowers and the songs of birds. Negotiating variability has been constant in people's lives. Long-term monitoring and detailed knowledge of other lifeforms and landscapes of people's home territories have assisted in responding and adapting to change. Aspects of cultural knowledge and practice that have helped Indigenous Peoples navigate nature's cycles at different scales of time and space include kin ties and social relationships, experiential learning, language, storytelling and timing of ceremonies such as "First Foods" celebrations. Working with ecological processes, Indigenous Peoples have been able to maintain optimal conditions for preferred species, reducing variability and uncertainty through taking care of productive habitats, leaving ecosystems intact, and allowing other species to change in their own cycles. Since the onset of colonization, however, Indigenous Peoples' lifeways have been changed drastically, culminating with the current impacts of global climate change and biodiversity loss. This paper, based on contributions of numerous Indigenous Knowledge holders from across Northwestern North America, outlines some of the key ways in which Indigenous Peoples have embraced predictability and change in their environments and lifeways, and addresses the particular threat of climate change: its recognition, ways of adapting to it, and, ultimately, how it might be reversed through developing more careful, respectful relationships with and responsibilities for the other-than-human world.
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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 | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".