25 Years of Criteria and Indicators for Sustainable Forest Management: How Intergovernmental C&I Processes Have Made a Difference
Bibliographic record
Abstract
Growing concern about forest degradation and loss, combined with the political impetus supplied by the Earth Summit in 1992, led to the establishment of eleven intergovernmental, regional, and international forest-related processes focused on the use of criteria and indicators (C&I) for sustainable forest management (SFM). Up to 171 countries have participated in these processes to apply C&I frameworks as a tool for data collection, monitoring, assessment, and reporting on SFM and on achieving various forest-related UN Sustainable Development Goals. Based on an expert survey and literature analysis we identify six interlinked impact domains of C&I efforts: (1) enhanced discourse and understanding of SFM; (2) shaped and focused engagement of science in SFM; (3) improved monitoring and reporting on SFM to facilitate transparency and evidence-based decision-making; (4) strengthened forest management practices; (5) facilitated assessment of progress towards SFM goals; and (6) improved forest-related dialog and communication. We conclude that the 25-year history of C&I work in forestry has had significant positive impacts, though challenges do remain for the implementation of C&I and progress towards SFM. The work should be continued and carried over to other sectors to advance sustainability goals more broadly.
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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 | 0.089 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".