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Record W4399318711 · doi:10.1139/cjss-2024-0023

<i>miyo wîcêhtowin</i> “good relations”: reckoning with the relationship between Indigenous Peoples and soil science in Canada

2024· article· en· W4399318711 on OpenAlexaffvenueabout
Melissa Arcand

Bibliographic record

VenueCanadian Journal of Soil Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIndigenous Knowledge Systems and Agriculture
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousGeographyEthnologyAnthropologySociologyEcologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.192
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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