The Ebb and Flow of Indigenous Rights Recognitions in Conservation Policy
Bibliographic record
Abstract
ABSTRACT At the 2003 World Parks Congress, diverse conservation actors called for the end of exclusionary approaches to conservation; recognition of customary forms of environmental protection; and restoration of losses to indigenous peoples whose lands were incorporated into protected areas without meaningful consent. A primary means to achieving such reforms has been the development of rights‐based approaches to conservation, expressed at the time as the better integration of human rights into the planning and management of protected areas. This article reviews the suite of publications that followed the 2003 Congress, each identifying the need for rights‐based approaches in conservation. All reviewed materials seek to operationalize human rights into conservation planning, but the review indicates a pattern of support for, then retreat from, and even a possible ‘backlash’ against, indigenous rights. The review also finds important differences in organizations’ ideas about who is responsible for protecting the environment versus who is responsible for protecting human rights. The authors draw from these findings a caution against the subversion of the original intention of rights‐based conservation to definitions that more fully serve conservation organizations’ own ends, based on the presumption that benefits (including rights) from environmental protection will eventually trickle down to people.
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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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".