Participatory modeling and analysis of sustainable forest management: experiences and lessons learned from case studies
Bibliographic record
Abstract
Participatory approaches to natural resource management and development have become widely accepted as the most effective instruments for achieving sustainable resource management particularly in the developing nations. This paper presents a participatory modeling framework that is consistent with participatory method of assessing sustainable forest management. Under this participatory modeling framework, a number of techniques have been developed aimed at: 1) communicating the concept of sustainable forestry to local communities, 2) soliciting direct input and active participation of local communities in the planning and decision-making process, and 3) seeking active involvement of local stakeholders in the formulation of the models and in their implementation for generating strategies and action plans. These models include: multi-criteria analysis, cognitive mapping, qualitative, and quantitative system dynamics. The models can be stand-alone models, or they can be combined together to constitute a more robust and flexible planning framework. These models have been applied to a number of case studies in the Philippines, Indonesia, Zimbabwe, and Ontario, Canada. Experiences and lessons learned from a selected set of applications are described in the paper.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".