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.
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 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 | 0.061 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.025 | 0.069 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".