Bibliographic record
Abstract
Indigenous Ways of KnowingAs Indigenous Peoples decolonize and attempt to survive Canada's ongoing genocide, there is a call to bring back to the table the Indigenous knowledge that processes of colonization pushed off, but is the space really being carved out?I, for one, am making space for it here with this book and with how I have come to write it or, in other words, my methods and methodology.I am Indigenist.As I previously discussed in my book Claiming Anishinaabe: Decolonizing the Human Spirit, Indigenous knowledge is a complete knowledge system that is grounded in its own assumptions, beliefs, theories, methodologies, methods, and practices.It stands to reason that Indigenous knowledge also has its own ways of creating, generating, preserving, and disseminating knowledge.These methods include prayer, song, dance, ceremony, heart knowledge, mind knowledge, personal knowledge, experiential knowledge, introspection, valuing personal and multiple truths, learning by doing, apprenticeship, practice, the Oral Tradition, memory, storytelling, listening, repetition, repetition, responsibility, respect, bravery, mentorship, helping, giving back, role modelling, caring, and valuing morality before knowledge.These methods are better known as Indigenous ways of knowing and being, and they remain intact today as a complete knowledge bundle-as opposed to being delineated in such a way that, for
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".