The effectiveness of public participation through forest advisory committees: Twenty years of shifting perspectives of Local Citizens Committee members in Ontario
Bibliographic record
Abstract
In Canada, the most widespread approach to citizen participation in forestry decision-making is forest advisory committees. In this paper, we report on the results of a survey of forest advisory committee members that has been repeated every few years since 2001. We clustered the survey questions to create composite variables representing seven dimensions of the functioning and effectiveness of forest advisory committees: efficiency, representation, voice, decision-making process, trust in forest managers, effectiveness, and the availability, relevance, complexity and trustworthiness of information. While most committee members continue to view the process positively, after a steady improvement of committee members’ overall assessments since 2001, in the most recent survey, the proportion of those who are unsatisfied has grown. Moreover, effectiveness has consistently been assessed less favourably than the other six dimensions that we considered—dimensions upon which effectiveness presumably depends. Part of the reason is that a significant minority of committee members feel that they have little meaningful influence over forest management and that the relevance of the committees has declined. This points to the need for continual attention from policymakers to the matter of how and how much forest advisory committees are enabled to influence the direction of forest management.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".