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Record W4387960333 · doi:10.17528/cifor-icraf/009006

Rights-Based Approaches in Climate Change, Conservation and Development Initiatives: Preliminary analysis and recommendations from a review of the scholarly literature

2023· review· en· W4387960333 on OpenAlexfundno aff
Léna Prouchet, Sarmiento Barletti

Bibliographic record

VenueCenter for International Forestry Research (CIFOR) eBooks · 2023
Typereview
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchConsortium of International Agricultural Research CentersWorld Agroforestry CentreEuropean CommissionUNICEFUnited States Agency for International Development
KeywordsClimate changePolitical scienceEnvironmental planningEnvironmental resource managementEngineering ethicsEnvironmental ethicsGeographyEnvironmental scienceEngineeringEcologyPhilosophyBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.336
GPT teacher head0.466
Teacher spread0.129 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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