Mindfulness, gratitude and sharing are central to berry harvesting practice, sustainability and adaptation in rural Alaska
Bibliographic record
Abstract
Berries are the most harvested and valued plant food in Arctic and sub-Arctic communities and are cultural keystone species in the North. Despite deep cultural significance and sustained use across generations, the relationships among berries and people in Arctic regions continue to be underexplored in published research. In this paper, we consider the ways in which people relate to berries and promote berrying in ‘cold’ places using a case study from a sub-Arctic community: Dillingham, Alaska. We posit that the environmental elements of cold climates and the ways that climate extremes shape plant stature and growth ultimately shape the ways people steward plants and landscapes. Interviews with 42 berry pickers identified 29 different practices of berry stewardship. We found the ways Yup’ik people express and enact care and responsibility towards berry plants is different from other Indigenous communities that have been widely documented in the Pacific Northwest of Canada and the United States. Stewardship is enacted through a deep respect for the bounty that landscapes naturally provide, cultural values of mindfulness on the landscape, sharing and reciprocity, spirituality and tradition, sustainability and access, and individual acts of biophysical stewardship and adaptation. We show how stewardship and berry relationships are shaped by the climatic and ecological conditions of a specific place, and we identify aspects of berry stewardship and relations unique to northern and cold regions.
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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 | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".