Toward Sustainable Self-Determination: Rethinking the Contemporary Indigenous-Rights Discourse
Bibliographic record
Abstract
More than eighty years since Chief Deskaheh petitioned the League of Nations for Haudenosaunee self-determination, it is becoming clearer that the existing rights discourse can take indigenous peoples only so far. States and global/regional forums have framed self-determination rights that deemphasize the responsibilities and relationships that indigenous peoples have with their families and the natural world (homelands, plant life, animal life, etc.) that are critical for the health and well-being of future generations. What is needed is a more holistic and dynamic approach to regenerating indigenous nations, and I propose the concept of sustainable self-determination as a benchmark for future indigenous political mobilization. Utilizing case studies of indigenous community regeneration such as the Native Federation of Madre de Dios (FENAMAD) in Peru and the White Earth Land Recovery Project (WELRP) on Turtle Island as well as analyzing the existing research on rights, political mobilization, and ecosystems, this article identifies alternatives to the existing rights discourse that can facilitate a meaningful and sustainable self-determination process for indigenous peoples around the world. Overall, findings from this research offer theoretical and applied understandings for regenerating indigenous nationhood and restoring sustainable relationships on indigenous homelands.
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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 | 0.022 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.093 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| 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".