Participatory decision support for sustainable forest management: a framework for planning with local communities at the landscape level in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it