Bolivia, Colombia & Canada : How the UN Declaration on the Rights of Indigenous Peoples Have and Have Not Been Adopted
Bibliographic record
Abstract
Approximately 15 years ago the UN Declaration on the Rights of Indigenous Peoples (UNDRIP) were signed, with 144 in favour, 11 abstentions and 4 rejections. The UNDRIP was ground-breaking, but the rejection from 4 powerful states (Canada, USA, New Zealand and Australia), and the subsequent lack of implementation decreased the expectations. This study sets out to investigate three states, Canada, Colombia, and Bolivia, and how they have implemented the declaration. Each state has cast a different vote on the declaration, which constructed a dissimilar stance on the UNDRIP. With a comparative research analysis, cases from each state will be reviewed through key-concepts from post-colonialism, such as hegemony, environmentalism, and place. Data is collected from national constitutions, court rulings and articles on the contrasting priorities of the government and the indigenous peoples. To measure the realization, three articles have been selected from the declaration. This paper concludes that even though the states have made substantial progress in legally adopting the declaration, practical realization lacks. This is due to the countries concern of losing political power were the indigenous peoples to gain self-determination or the inability to conduct extractive projects on indigenous territory which would increase national income.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".