Bibliographic record
Abstract
There is a global consensus that stopping deforestation is crucial for planetary health. Global efforts to curb deforestation, such as the Paris Agreement, the United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD) programme, and the aspirational New York Declaration of Forests, involve significant international and cross-sectoral coordination. They also involve the creation of new institutions and governance mechanisms to accomplish the goals set out in these instruments. At the same time, national-level efforts to support human development, reflected in the United Nations (UN) Sustainable Development Goals (United Nations, 2016a, 2016b), aim to increase the welfare and wellbeing of populations living in poverty. Meeting these development goals will inevitably have cross-cutting effects on initiatives to address deforestation. In balancing these goals, policy-makers are confronted with wicked problems – or problems where there are moral considerations and where limited information is available for policy-makers. This book is focused on how wicked forest policy problems have been, and can be, addressed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".