Monitoring changes in forest resource advisory groups’ composition and evaluations of perceptions of public participation effectiveness: a case of Ontario’s Local Citizens Committees
Bibliographic record
Abstract
Effective public participation is a key part of sustainable forest management on publicly owned lands. However, long-term monitoring data that seek to measure effectiveness of public participation in forest management planning is lacking. Here, measures based on attitudes and satisfaction ratings associated with suspected criteria of public participation effectiveness were developed and applied to forest resource advisory group members from Ontario, Canada. Using data from four social surveys (2001, 2004, 2010, and 2014), advisory group members were, on average, satisfied and held positive attitudes towards the advisory group, their participation in the group, and forest management planning. In many instances, these positive evaluations increased from 2001 to 2014, especially for statements related to fairness. One concern about Local Citizens Committees (LCCs) related to their composition. Advisory group members were male dominated (about 88%) and were increasingly overrepresented by individuals between 50 and 69 years old in 2014 (67%). Given that male and female LCC members held different perceptions of the effectiveness of some public participation criteria, these concerns suggest that composition of LCCs might impair the ability of the groups to consider all viewpoints related to forest management planning. Finally, the research illustrates the importance of designing and collecting long-term monitoring data to understand how evaluations of public participation and composition of participants changes over time.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".