Navigating Indigenous Self-Government in Canada: The Case of Nunavut and the Struggle for Institutional Sovereignty
Bibliographic record
Abstract
This study critically examines the evolution of Aboriginal self-government in Canada, with a particular focus on the case of Nunavut. Drawing from historical and contemporary perspectives, it explores the complex relationship between Indigenous self-determination, Canadian federalism, and socio-economic challenges. The analysis highlights the enduring struggle of Indigenous peoples to assert their sovereignty, despite federal intervention and economic dependency. The study evaluates key policy areas, such as language and education, and assesses their role in nation-building efforts within the Inuit governance model. While Nunavut’s self-government demonstrates significant autonomy in administrative and cultural affairs, financial reliance on the federal government raises questions about the extent of its sovereignty. Through comparative insights from other Indigenous governance structures and Quebec’s constitutional position, the paper argues that self-government can be strengthened through institutional capacity-building and expanded self-determination rights. Ultimately, it underscores the importance of land claims as a mechanism for enhancing institutional sovereignty without necessitating political secession from Canada.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.051 | 0.018 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".