Indigenous food systems and food sovereignty: A collaborative conversation from the American Association of Geographers 2022 Annual Meeting
Bibliographic record
Abstract
Indigenous scholars and their allies increasingly contribute to food systems debates and practices through pursuing and interrogating ideas of Indigenous food sovereignty. This essay adds to this ongoing conversation by providing a synthesis of and reflection on a panel session on Indigenous food sovereignty held at the American Association of Geographers (AAG) 2022 Annual Meeting. We place this conversation in the context of a growing body of scholarship on food sovereignty and Indigenous food systems. Organized by the AAG’s Geographies of Food and Agriculture Specialty Group, with support from the Journal of Agriculture, Food Systems and Community Development, the session engaged Indigenous scholars in a discussion about the meaning of food sovereignty, different ways of knowing, relationships and reciprocity, and systems of power. The panelists emphasized the relationship between all elements of creation at the core of food sovereignty, the importance of valuing different ways of knowing and expertise, making visible histories of settler knowledge appropriation, and critically assessing how power manifests, operates, and is understood in different food systems and worldviews. Building on the scholarly literature and the evolving place-based grounding of food sovereignty movements, we argue that it is critical to address ongoing realities of genocide and settler colonialism in North America/Turtle Island by forging respectful relationships with all of creation and to work through collaborations led by Indigenous people and grounded in reciprocity.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.037 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.010 | 0.015 |
| 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".