The Nêhiyawak Nation through Âcimowina: Experiencing Plains Cree Knowledge through Oral Narratives
Bibliographic record
Abstract
This paper connects you to the Nehiyawak Nation of Western Canada by sharing key elements of our intellectual traditions and knowledge systems that have been shared through countless generations. The basis of this paper extends through the Indigenisation and decolonisation initiatives of preceding Indigenous scholars who have begun sharing their knowledge in a means to rewrite and challenge the dominant systems and methodological approaches that we ourselves find ourselves in. Since it does, I want you as the reader to understand that the concepts found in this paper are not the typical knowledge you are taught, and I ask you to come with an open mind as you read it since our Western society has been removed from metaphysical elements of our environment. The stories and insights of the Nehiyawak culture found in this paper delivers an emphasis on the deep-rooted understanding of the universe linked within our language, understanding of the world, and ancestral knowledge. It is shared with you for the purpose of enlightenment, and not for academic debate. I do not own this knowledge, but these are the stories of the Nehiyawak Nation, of which I am a part. You may not understand this paper now, but the information presented and given to you will keep with traditional customs and will make sense to you maybe not this year but maybe couple years down the road, as this is the lifelong effect of storytelling found in the Nehiyawak Nation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".