Practice-Based Knowledge for REDD+ in Vanuatu
Bibliographic record
Abstract
The rural populations of small island developing states in the Pacific region are amongst the most exposed to the harsh realities of climate change. Forest management, tree planting, and agroforestry are some of the most promising strategies to build local resilience while providing food and income security in these remote areas. In this paper, we outline the contextual reasons for why deforestation and forest degradation continues, and provide practice-based approaches for REDD + to address deforestation. Our transdisciplinary methods include the construction of seven land use models to compare business-as-usual scenarios with respective REDD + strategies across Vanuatu’s five REDD + islands, combined with a case study of Vanuatu’s first REDD + project. Close collaboration between international researchers, local government officials, and Ni-Vanuatu non-governmental organizations and communities allowed for information sharing across epistemologies, adding local, place-based knowledge to scientific inquiry, responding to calls for more ‘locally led’ approaches to climate adaptation in Vanuatu.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".