Amazonian visions of Visión Amazonía: Indigenous Peoples' perspectives on a forest conservation and climate programme in the Colombian Amazon
Bibliographic record
Abstract
Abstract Although Indigenous Peoples' rights to own, control and manage their lands and territories are well established under international law, Indigenous Peoples affected by forest conservation and climate protection programmes continue to denounce interventions that fail to uphold their rights. This article focuses on the internationally funded Visión Amazonía REDD Early Movers programme in the Colombian Amazon. Drawing on observations and critiques by Indigenous rightsholders in the Middle Caquetá River and human rights insights from a legal complaint raised by one Indigenous community against the programme, we demonstrate the programme's inadequate protection of collective rights, especially relating to the fundamental right to free, prior and informed consent and the resulting inequitable benefit sharing. We focus on conflicting views between Indigenous and non-Indigenous actors over the definition of direct effects on Indigenous Peoples (which triggers the requirement for prior consultation and consent), the basis for inclusion of Indigenous Peoples as programme beneficiaries, and the role accorded to Indigenous science in such programmes. Notions of permission and consent in the customary law and economic practices of the concerned Indigenous Peoples are central to the conviviality and reproduction of human and non-human societies within their territories. To ensure more accountable and sustainable international environmental finance and conservation interventions, and to ensure respect for Indigenous Peoples' self-determination and territorial and cultural rights, we recommend that these initiatives adopt human rights-based, pluri-legal and intercultural approaches centring on the right to free, prior and informed consent as a structuring principle. Additionally, we call for more robust measures in forest and climate protection programmes, to recognize and respect customary law, collective property, traditional livelihoods and Indigenous science.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".