Biocultural memory of reciprocity: the Mapuche trafkintu as social-ecological relationships of care and vindication
Bibliographic record
Abstract
Reconsidering the relationship between humans and more-than-human beings amid global crises has brought reciprocity practices between people and biodiversity to the forefront. We examine social-ecological reciprocity practices within Indigenous territories and their direct connection to biocultural memory. Specifically, we explore the Mapuche practice of trafkintu in the Andean zone of Wallmapu, La Araucanía region of southern Chile. Using a mixed-methods framework, from a relational perspective, we integrate spatial analysis of a seed exchange network involving 80 local farmers, with an ethnographic and collaborative phase with 12 Mapuche women-who are part of this network-over three years. We found that social-ecological reciprocity practices-like those in the trafkintu-are constitutive of a biocultural memory. This biocultural memory has been vital for sustaining and transforming social-ecological reciprocity practices amid colonial and neo-colonial pressures. We term this recursion the "memory of reciprocity." This provides key insights into how reciprocity manifests as a quality of complex social-ecological relationships, marked by mutual care among people, seeds, and other more-than-human beings. It also helps us understand how, amid the colonialism and dispossession endured by Indigenous peoples for centuries, reciprocity has been essential to survival and vindication.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".