Our lands tell our stories: supporting Indigenous co-led research through the Indigenous Foods Knowledges Network
Bibliographic record
Abstract
The Indigenous Foods Knowledges Network (IFKN) brings together Indigenous researchers and community leaders from the Arctic and U.S. Southwest along with non-Indigenous researchers to foster cross-cultural interdisciplinary knowledge exchange about sovereignty of Indigenous foods. IFKN draws on cultural and scientific expertise from shared cultural protocols and practices, Indigenous Knowledges, Earth sciences, and social sciences to better understand reclamation, preservation, and perpetuation of traditional food practices to sustain Indigenous food sovereignty in a rapidly changing global environment. In this article, we discuss how IFKN developed a methodology prioritizing relational accountability encompassing both people and place while establishing a framework for collaborative learning that centers Indigenous Knowledge systems. We provide examples from our roles as Tribal community members, university researchers, and network members in creating an organizational framework for this collaborative work and connecting it to community, university, and research protocols and practices. We further describe the ways that IFKN adapted during the COVID-19 pandemic to continue to remotely co-produce knowledge and amplify concerns and priorities of community partners through non-academic settings.
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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.037 | 0.020 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".