Indigenous Knowledge Within Academia: Exploring the Tensions That Exist Between Indigenous, Decolonizing, and Nêhiyawak Methodologies
Bibliographic record
Abstract
Over the last few decades the rewriting of Indigenous knowledge and history has been discussed, debated, and rewritten through the fields of Anthropology, History, and First Nation Studies, to name a few. One of the main tensions that exists in this reclamation process is the differences between Indigenous and Western methodological approaches. However, it has yet to be put forward as to what are the tensions that exist within Indigenous methodologies and their practice. This paper will bring forward three methodological approaches utilized within research for and by Indigenous peoples, as we examine how Indigenous, Decolonizing, and Nêhiyawak methodologies challenge and support one another, and how in order to conduct research, specific views must be taken into account to give a better understanding of the philosophical and spiritual foundations in which the research is situated. Specifically, the article will assess what are Indigenous, Decolonizing, and Nêhiyawak methodologies and why there is a need to incorporate specific methodological approaches dependent on the research in question. Yet, in order to understand the importance and relevance of these differing approaches to find knowledge, we must first discuss how early research and ethics impacted what we know about Indigenous peoples and their way of life. I focus on Nêhiyawak methodologies in particular as a member of the Nêhiyaw Nation in the territory of Maskwacîs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".