25 Years of Criteria and Indicators for Sustainable Forest Management: Why Some Intergovernmental C&I Processes Flourished While Others Faded
Bibliographic record
Abstract
The use of criteria and indicators (C&I) for data collection, monitoring, assessing and reporting on sustainable forest management (SFM) has been growing since the Earth Summit in 1992, supported by eleven intergovernmental, regional and international forest-related C&I processes. The initial effort led to varying levels of implementation across countries. Several processes never went much beyond the adoption of a first set of C&I while others have made substantial progress. In recent years, interest in C&I for SFM has again increased. In light of the Sustainable Development Goals and emerging global challenges the contribution of C&I to monitor, assess and report on forest conditions and trends is increasingly important. We compare and analyse the structure, activities and progress of the intergovernmental C&I processes. The work is based on document analysis and questionnaires sent to the secretariats of the processes and C&I experts. We found many similarities but also major differences in the structure and content of the C&I sets. The results provide a context for discussing and understanding why some of the C&I processes are successful in their work while others have stalled. Finally, we propose the required ingredients for success for the future activities of the forest-related intergovernmental C&I processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".