Plant Management Systems of British Columbia’s First Peoples
Bibliographic record
Abstract
This paper provides an overview of the diverse plant resource management strategies of First Nations of BC. Contrary to the predominant “hunter-gatherer” designation by anthropologists and others, First Peoples of many parts of the province were actually astute managers of plant and animal resources. Over thousands of years, they developed a wide range of strategies and techniques – from periodic burning of landscapes, to pruning berry bushes, tilling and selective harvesting – to maintain and enhance the quality and quantity of their resources. There are numerous examples of plant species and habitats for which various types of management have been applied. Three case studies are provided here: Culturally Modified western red-cedar trees; estuarine root gardens; and orchard-gardens from an ancient village site in Tsimshian territory. Over generations, as people’s knowledge bases, social systems and technologies mature, plants and environments become embedded into complex belief systems, in which cultural control becomes encoded in stories, taboos, ceremonies, art and ethics. The complexities of this last layer of culturally proscribed management are still little understood, but may be the most significant component of traditional management systems, allowing for the development and maintenance over a long time period of sustainable anthropogenic landscapes. Many aspects of indigenous management systems need further investigation, including ways in which they may be effectively applied in a contemporary world as a way of enhancing and supporting Indigenous peoples’ food security, land rights and continued cultural development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".