Indigenous caretaking of beargrass and the social and ecological consequences of adaptations to maintain beargrass weaving practices
Bibliographic record
Abstract
Indigenous ecologies have persisted through major social and ecological changes including settler colonialism. Adaptations have been a necessary part of this resilience, however little attention has been given to the consequences of these adaptations for Indigenous Peoples and ecologies. Without exploring these consequences, we are left with an incomplete understanding of adaptation that potentially obscures social and ecological costs associated with resilience. Here we describe the contemporary caretaking of a culturally-significant plant used in weaving traditions called beargrass (Xerophyllum tenax Melanthiaceae), and discuss how adaptive practices to maintain biocultural connections to beargrass have influenced both socio-cultural and ecological systems. We ask: (1) How is beargrass stewarded and used today? (2) What are the adaptive practices that Indigenous communities in the Pacific Northwest have used to maintain cultural traditions through changing conditions? (3) What are some of the social and ecological consequences of these adaptations? Through semi-structured interviews with cultural practitioners we identified multiple reciprocal practices that form a basis of the caretaking relationship. In order to compensate for a lack of access to beargrass and lack of ability to exercise sovereignty in land management, practitioners described substituting other weaving materials for beargrass, as well as caretaking substitutions. These adaptations were not uniformly accepted and for some either represented significant cultural losses or placed additional burdens on communities. We also collected ecological field data on beargrass. Using structural equation modeling, we found that a key adaptive practice, the substitution of tree pruning for cultural fire, can replicate key short-term benefits of fire for beargrass populations, but does not appear to replicate longer term benefits. In sum, adaptive practices have allowed beargrass traditions to persist through colonialism, but cannot fully substitute for social and ecological benefits of pre-colonial caretaking, and also result in losses and/or additional burdens for communities. Investigating what adaptations to maintain resilience do in communities, and for whom, is necessary in order to fully appreciate the costs and benefits of adaptations that support resilience through various forms of perturbation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".