Rights-Based Approaches in Climate Change, Conservation and Development Initiatives: Preliminary analysis and recommendations from a review of the scholarly literature
Bibliographic record
Abstract
Rights-Based Approaches (RBAs) purposefully position the recognition of, respect for, and access to individual and collective rights as central to an initiative’s planning, design, implementation, monitoring process, and outcomes. In mainstream climate change, conservation, and development programs and policies, this means refocusing the relationship between ‘beneficiaries’ and ‘implementers’ to one of right holders and duty-bearers. RBAs hold growing discursive importance in relation to the rights of Indigenous Peoples and local communities (IPs and LCs) in conservation and climate change spheres and the agendas of international agencies. The growing interest in RBAs, and their inclusion in frameworks that will guide development, conservation, and climate projects over the next decade is laudable. However, there is a shortage of analysis of RBA experiences, both their conceptualization and practice. Such analysis would advance discussions on the impact of these approaches and provide lessons to enable transformative change. This review is a preliminary assessment that aims to advance the ongoing conversation on RBAs. Our primary interest is the conception and implementation of RBAs in forest-based initiatives, but we reviewed the wider scholarly and gray literature on RBAs in development, conservation, and climate action initiatives. The review was complemented by interviews with a multi-actor group of specialists and advocates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.029 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".