Consider <i>bagaan</i> : The importance of hazel to Indigenous Peoples of eastern North America and implications for sovereignty
Bibliographic record
Abstract
Abstract Bagaan in Ojibwemowin, or the American and beaked hazelnut ( Corylus americana and Corylus cornuta var. cornuta : Betulaceae), constitutes important food, medicine and cultural resources for Indigenous Peoples across eastern North America and is also a vital source of food and habitat for wildlife in ecosystems where it occurs. Hazels have been actively stewarded within numerous ecosystems, and within the Great Lakes Region of the United States and Canada, this stewardship historically was done with fire by Indigenous Peoples. Through colonization practices and the criminalization of fire, the role of hazels in forests and foodways has greatly shifted. There has been increased interest in the commodification of hazel through farming and genetic modification of hazel through gene mapping which impacts the inherent sovereignty of this species. Central to this discussion are the memories and perspectives of Elder Benesikwe (Shirley A. Nordrum) of the Martin Clan from the Red Lake Nation of Minnesota. This study offers an example of Earth stewardship in practice by centering Indigenous relationships with Bagaan —hazelnut—as living threads that weave together ecology, culture, and governance. By illuminating how Ojibwe stewardship practices such as fire management of hazel sustained both its role in ecosystems and foodways, and by tracing how colonization and commodification disrupted those relationships, this work extends the concept of stewardship beyond management to encompass both People and species. Through the voice and memories of Elder Benesikwe, and by integrating ethnohistorical, botanical, and biodiversity data, we draw connections between People and place by emphasizing Indigenous ways of knowing hazel as an important ecosystem member and coproduce knowledge across Indigenous and Western scientific traditions. This contribution demonstrates how revitalizing place‐based stewardship can inform equitable conservation and adaptation efforts worldwide.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".