Assessing the effects of public participation processes from the point of view of participants: significance, achievements, and challenges
Bibliographic record
Abstract
Public participation practices are now common and recognized as a way of including a broader range of interests andsocial values in forest management. However, we know little about their real benefits. This article presents the results of astudy aimed at developing a deeper understanding of the diverse impacts of public participation and, in particular, of forest-related deliberative forums (i.e. committee types of processes). The study is based on an analysis of data collected from137 respondents–participants and coordinators–who have been involved in more than 120 forest-related public participationprocesses in the province of Quebec. The study examined the diversity of potential impacts of public participationprocesses, assessed the significance of the impacts, and evaluated the capacity of existing processes to achieve them.Overall, the study provides practical information to support the evaluation of public participation processes, a requirementthat is increasingly imposed on forest practitioners and decision-makers.Key words: forest governance, forestry, outputs/outcomes, impacts of citizen involvement/public participation processes,stakeholder consultation, advisory committees, evaluation, performance measurement, criteria and indicators, sustainableforest management, Canada, Quebec
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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 | 0.264 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".