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Record W4389302772 · doi:10.53892/tulf5751

Workshop Report: Advancing Community Rights in Area-based Conservation

2023· report· en· W4389302772 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Convention on Biological DiversityNegotiationPolitical scienceEnvironmental planningUnited Nations Framework Convention on Climate ChangeAllianceEnvironmental resource managementBiodiversity conservationCapacity buildingAction (physics)BiodiversityGeographyConventionClimate changeEcologyEnvironmental science

Abstract

fetched live from OpenAlex

The Convention on Biological Diversity Conference of the Parties (COP15) held in late 2022 marked the adoption of the Kunming-Montreal Global Biodiversity Framework (GBF) and its main area-based conservation target, which calls for conservation of at least 30 percent of terrestrial, inland water, and coastal and marine areas by 2030 (commonly referred to as 30×30). Central to this target and to the Framework in general was the recognition of the significant contributions of Indigenous Peoples and local communities to biodiversity conservation. The adoption of the GBF has opened new opportunities and risks for implementing its area targets in keeping with community-led and rights-based approaches to conservation (RBAs). With the negotiations now concluded, it is essential to shift attention and action to the implementation and monitoring of RBAs for these area targets, especially at country levels. This also requires building increased mutual understanding and collaboration across key constituencies and organizations that can support rights-based conservation. In this context, Rights and Resources Initiative (RRI), Campaign for Nature (C4N), the ICCA Consortium, and the Global Alliance of Territorial Communities (GATC) organized a one-day workshop during the New York Climate Week, 2023 aiming to mobilize collaboration and action on RBAs for the implementation and monitoring of 30×30 targets. This report compiles the key message of this discussion.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.083
GPT teacher head0.348
Teacher spread0.265 · 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.

Study designNot applicable
Domainnot available
GenreOther

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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