From “taking” to “tending”: learning about Indigenous land and resource management on the Pacific Northwest Coast of North America
Bibliographic record
Abstract
Abstract Indigenous peoples have occupied the northwestern North American coast for at least 15 000 years—a time when much of the land was covered by a kilometre or more of ice and only patches of land were glacier free. Over the millennia, through difficult times and seasons of plenty, they have built up an immense body of local knowledge, practice, and belief—Indigenous, or Traditional Ecological Knowledge—enabling them to live well, learning about the plants and animals of terrestrial, aquatic, and marine environments on which they have depended, and how to harvest and process them into nutritious foods, healing medicines, and useful materials. Although it has been commonly assumed that these people, as so-called “hunter-gatherers”, were simply helping themselves to nature’s provisions, over decades of learning from Indigenous plant specialists and other knowledge holders as an ethnobotanist, I have come to see First Peoples as resource tenders and managers over countless generations. Their traditional land and resource management systems provide many lessons on how we humans can work with natural processes to ensure the well-being not only of ourselves but also of the species and habitats on which we rely.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".