Stories from Guatemala and North America: Why Indigenous Beliefs Matter in the Debate on Genetically Engineered Food
Bibliographic record
Abstract
Indigenous peoples do not share a common religion, but almost all share a history of colonialism and Christian missionization. Cultural practices relating to food production and consumption have been central to preserving and transmitting to future generations local ecological knowledge, social institutions, ethnic identity, and spiritual teachings. Food practices, including prohibitions, evince an extraordinary body of knowledge passed down orally over generations, and these practices have been increasingly threatened by a widespread transition from locally produced and prepared food to the consumption of marketed, globally sourced, refined, and processed foods. According to Debra Harry, Director of the Indigenous Peoples Council on Biocolonialism, genetically engineered foods represent to indigenous peoples worldwide both the extension of an on-going colonial destruction of their local knowledge systems and a violation or desecration of the natural world. While genetically engineered seeds are being marketed and distributed around the world through agribusiness, food aid programs, and government agents, little attention has been paid by scholars to the religious and spiritual reservations of indigenous peoples to this technology. In this paper, Born from Bears and Corn: Why Indigenous Beliefs Matter in the Debate on Genetically Engineered Food, written with Lorenzo Magzul and Nancy Turner for a forthcoming book anthology of religious perspectives on genetically engineered food, we explore the ways that indigenous food practices are tied inextricably to sustaining ecological health often through food taboos, prohibitions, and spiritual teachings that impart a respect for the integrity of the earth and its life-forms. These complex forms of knowledge and socialities represent world fviews that integrate and inform community organization and are based on direct dependence on the natural world. In this paper, we selected two research methods we felt most effectively narrate particular indigenous perspectives on transgenes in food. Lorenzo Magzul and Shiri Pasternak travelled to Guatemala to conduct focus groups in Magzul's hometown of Patzun with the local Mayan community and Nancy Turner sent out questionnaires to indigenous people in North America. Thirteen people participated in the North American focus group, including six males and seven females. Of these, over eight different North American indigenous nations were represented, including: Cherokee Nation, Wasco (Warm Springs Confederacy, and other nations), Gwich'in, Metis, Mohawk, Haida, Nuu-Chah-Nulth (three different communities) and Straits Salish (two communities). …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".