How to Decolonize Democracy: Indigenous Governance Innovation in Bolivia and Nunavut, Canada
Bibliographic record
Abstract
This paper analyzes the successes, failures, and lessons learned from the innovative experiments in decolonization that are currently underway in Bolivia and Nunavut, Canada. Bolivia and Nunavut are the first large-scale tests of Indigenous governance in the Americas. In both cases, Indigenous peoples are a marginalized majority who have recently assumed power by way of democratic mechanisms. In Bolivia, the inclusion of direct, participatory, and communitarian elements into the democratic system, has dramatically improved representation for Indigenous peoples. In Nunavut, the Inuit have also opted to pursue self-determination through a public government system rather than through an Inuit-specific self-government arrangement. The Nunavut government seeks to incorporate Inuit values, beliefs, and worldviews into a Canadian system of government. In both cases, the conditions for success are far from ideal. Significant social, economic, and institutional problems continue to plague the new governments of Bolivia and Nunavut. Based on original research in Bolivia and Nunavut, the paper finds that important democratic gains have been made. I argue that the emergence of new mechanisms for Indigenous and popular participation has the potential to strengthen democracy by enhancing or stretching liberal democratic conceptions and expectations.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".