Embodying Milpa: Centering Place to Cultivate Polycultures of Reciprocity in Learning Environments
Bibliographic record
Abstract
Milpa is an ancestral agriculture technique that has been passed down by Indigenous communities in so-called North America for millennia through stories of place. As an Indigenous knowledge system that is based on the symbiotic cultivation of diverse species, learning from milpa gifts lessons to cultivate polycultures of reciprocity. Drawing from my lived experience as a Mestizx educator from Mexico, I propose embodying milpa in learning environments as a horizon of possibility to refuse and disrupt the ways in which education has been limited, standardized, controlled and confined to colonial monocultures (Shiva, 1993). I use the cultivation of milpa as a metaphor to represent the transformation of modernist education systems that prioritize individuality and rationality shifting towards collaborative learning environments that are complex, messy and entangled with the land. Late Indigenous Professor Michael Marker (2018) invites scholars to center place as the beginning point of inquiry when excavating the specific effects of colonization on Indigenous landscapes and communities. Following his advice, I use stories of milpa to experience placeness and provide a representation of the transformation of modernist education systems through the resurgence of Indigenous knowledge systems. I begin this paper with a piece of the Mexica creation story to center “the consciousness of landscape” (p. 453) and continue to explain how from a modern perspective, place has shifted to produce bordered monocultures that eradicate diversity. I conclude by narrating the story of three sisters to envision the embodiment of milpa inside learning environments to form polycultures of reciprocity where animate and inanimate beings are all connected inside a web of relations belonging to the land.
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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 | 0.001 | 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".