Bridging tradition and innovation: strengthening food system resilience through Indigenous Guardian partnerships and knowledge sharing in the Sierra Nevada and British Columbia
Bibliographic record
Abstract
The vitality of an Indigenous food system is directly tied to how well a community can access and care for the lands and waters they have historically stewarded. In both California’s Sierra Nevada region and British Columbia, Indigenous communities face urgent climate-related impacts, including catastrophic wildfires and drought, which threaten traditional food systems and cultural landscapes. This community case study explores the knowledge sharing efforts and decision support tool development of First Nations (British Columbia), and Indigenous communities in the Sierra Nevada region in California, and academic partners to support the expansion of community-led land and water stewardship. Through Indigenous Guardian programs, participatory mapping, two-eyed seeing, and data sovereignty principles such as Ownership, Control, Access, and Possession (OCAP®), these partnerships strengthen Indigenous governance structures while addressing historical land dispossession and disrupted foodways. Restoration of lands and waterways is a prerequisite for achieving food sovereignty, necessitating cultural fire practices, improved access to ancestral lands, and Indigenous-led policy interventions. Elders and Knowledge Bearers play a critical role in transmitting Indigenous Knowledge (IK) through oral traditions and hands-on stewardship, reinforcing the importance of intergenerational learning and community-driven processes. Furthermore, this case study underscores the need to create Indigenous-led spaces for knowledge-sharing, collaboration, and policy engagement that prioritize Indigenous voices, sovereignty, and self-determination. By integrating IK with emerging technologies and policy frameworks, Indigenous communities in California and British Columbia are not only restoring stewardship rights by reclaiming their own data but also shaping resilient, climate-adaptive food systems. This paper advocates for sustained investment in Indigenous governance, intertribal collaboration, and equitable decision-making processes that support the continuation of traditional foodways for future generations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".