Bibliographic record
Abstract
Indigenous nations have diverse, complex, and ancient governance theories and practices, yet settler governments have consistently tried to eliminate these theories and practices. Despite the objectives of colonization, Indigenous people have maintained the knowledge of Indigenous governance. To understand Indigenous governance, an effort must be made to understand these theories from a specific Indigenous worldview. In other words, what is Indigenous governance in Indigenous terms? This dissertation aims to address this question by exploring governance through the knowledge of the Nehinuw by asking, what does Nehinuw knowledge teach us about Nehinuw governance? To understand Nehinuw governance from a Nehinuw worldview, the author researched using a Nehinuw theoretical framework which included specific Nehinuw research methods and a method analysis based on the Nehinuw concept of Nistotên (to understand). The findings of this dissertation consider the complexity and diversity of Nehinuw governance theory and practice that challenge mainstream perspectives of Indigenous governance and provide valuable lessons for policymakers that work in the field of Indigenous governance. The outcome of this dissertation fills more than a gap in the literature because using the Nehinuw theoretical framework has enabled me to empower the communities covered in this dissertation, and develop, in partnership with local educators, educational resources on Nehinuw governance that can and will be used by the community to educate future generations on Nehinuw governance and become the foundation of future scholarly research and practice. Keywords: Indigenous Governance, Indigenous Knowledge, Nehinuw Governance, Nehinuw Knowledge
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".