<i>miyo wîcêhtowin</i> “good relations”: reckoning with the relationship between Indigenous Peoples and soil science in Canada
Bibliographic record
Abstract
I offer this perspective as hope that miyo wîcêhtowin (translated as “good relations” in Plains Cree) can be established between the discipline of soil science and Indigenous Peoples in Canada. This perspective reflects not only on the difficult truths of why the relationship between Indigenous Peoples and soil science is primarily one of exploitation and neglect, but also on how fostering a relationship built on reciprocity presents opportunities for Indigenous knowledge systems and soil science to improve the way we relate to land and how we steward soil. Soil science was borne in this country as an instrument of colonization of the plains, marginalizing First Nations from their lands and livelihoods through agricultural settlement. It is necessary to illuminate this fraught history to understand the contemporary realities of First Nations in the prairies, including the hopeful efforts First Nations are making towards conservation and restoration of prairie landscapes—and revitalization of Indigenous knowledge systems—especially though buffalo rematriation. This sharing is done in the hope that we can collectively work towards reciprocity in the relationship between Indigenous Peoples and soil science as a discipline for improved caretaking of the land.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".