Public and stakeholder involvement in forest governance: rethinking the forest advisory committee approach in Canada
Bibliographic record
Abstract
The Forest Advisory Committee (FAC) is a popular governance structure in Canada for governments and forest companies to engage public and local stakeholders in forest management planning. These committees are established across Canada to incorporate diverse values in forest decision-making and to move towards Sustainable Forest Management (SFM). However, scholars have noted many problems and shortcomings associated with these committees and suggest there is a need to rethink the FAC approach for involving the public and stakeholder organizations in forest management by developing a new framework for such involvement. This study 1) investigate how various jurisdictions involve the public and stakeholders in forest management decisions, 2) identify eading-edge approaches for involvement in forest management decisions that are more democratic and deliberative, 3) examine ways the public and stakeholders, other than forest product companies and government, can be incentivized to be involved in forest management, 4) develop a framework for involvement in forest decisions in Canada in the context of the tenure approach that captures the findings related to the above objectives and ensures greater public involvement. The study take a qualitative case study approach, utilizing document review, semi-structured interviews, and thematic analysis. Overall, the data reveal that public participation in forest governance in Canada is weak, especially at the strategic and normative levels of decision making. The results of this study suggest a three-level framework for engagement, starting from forest policy and normative decisions at the top, to strategic decisions in the middle, and operational decisions in the bottom for public and stakeholder involvement. Each level requires its own distinct programs for participation, utilizing different tools and techniques, mainly because each needs to involve a different range of participants and address different forestry issues. However, overlap of some of the participants in each process and linkages between participation programs is also highly desirable. The FAC approach is not an appropriate model for getting public input to all these levels of decisions, but with some improvements it may still have utility in relation to forest management decisions that are operational in nature.
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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 | 0.058 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.051 | 0.021 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| 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".