Bibliographic record
Abstract
This chapter illustrates a non-anthropocentric approach to conducting research utilising the epistemological views of an Anishinabe Kwe researcher from Canada. The chapter will illustrate how the Anishinabe oral tradition of transferring Indigenous knowledge intergenerationally was used to create a research methodology reflecting the non-anthropocentric views that Anishinabe possess towards the natural world. This methodology is inspired by the Anishinabe Waterdrum. It is utilised to research residential schools, which are one part of the ongoing settler colonialism of Turtle Island. The sacred teachings of the Anishinabe situate all of Creation at the centre of the universe. This is exemplified within the Creation Story, which teaches that all living things were placed upon Mother Earth before human beings. The plants, animals, rocks, rivers, fish, birds, and trees were placed here before humankind to ensure that humans would have everything they needed to survive and prosper. This does not mean the Creator placed all living things before mankind for their use and exploitation. Rather, these entities were placed on Mother Earth to co-exist and live in harmony. As an Anishinabe, it is unfathomable to contemplate that humankind would consider themselves the most significant species on the planet when human existence is dependent upon all living things placed upon Mother Earth by the Creator. Without the necessities found within the natural world, humans would not survive.
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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 | 0.046 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".