Participatory decision support for sustainable forest management: a framework for planning with local communities at the landscape level in Canada
Bibliographic record
Abstract
There is an increasing demand for active public involvement in forestry decision making, but there are as yet few established models for achieving this in the new sustainable forest management (SFM) context. At the level of the working forest, the fields of forest sustainability assessment, public participation, decision support, and computer technology in spatial modelling and visualization need to be integrated. This paper presents the results of a literature review of public participation and decision-support methods, with emphasis on case study examples in participatory decision support. These suggest that emerging methods, such as public multicriteria analysis of alternative forest management scenarios and allied tools, may lend themselves to public processes addressing sustainability criteria and indicators. The paper develops a conceptual framework for participatory decision support to address the special needs of SFM in tactical planning at the landscape level. This framework consists of principles, process criteria, and preliminary guidelines for designing and evaluating SFM planning processes with community input. More well-documented studies are needed to develop comprehensive, engaging, open, and accountable processes that support informed decision making in forest management, and to strengthen guidance for managers.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".