The global implementation of UNDRIP: a thematic review
Bibliographic record
Abstract
Following almost 25 years of work, the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) was adopted by the United Nations General Assembly in 2007. UNDRIP has been widely recognised as an authoritative statement of human rights norms concerning Indigenous peoples around the world. However, meaningful implementation of UNDRIP has been slow. To better understand the pace and challenges facing implementation, we identify and analyze four key recurring themes that emerge from the growing literature on this topic. This includes (1) Indigenous self-determination versus state autonomy as a driver of potential conflict; (2) the meaning of Free, Prior, and Informed Consent (FPIC) in the context of UNDRIP; (3) the nexus between land, culture, and self-determination; and (4) Western/Global North influence over non-Western/Global South state implementation of UNDRIP. We examine several specific examples from across the globe to reveal the continuities and discontinuities across these four themes. The article concludes by offering ideas on how the limits of the present study and wider literature on UNDRIP might be rectified. That is, by incorporating a greater number of Indigenous perspectives on UNDRIP and adding more studies of governance and implementation challenges encountered in countries located in the Global South.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".