Public participation in community forests : the Ontario community forest pilot projects / by Florence Wanjira Chege.
Bibliographic record
Abstract
Levels of public participation in the management of a community forest (CF) \ndepend on the CF?s goals and the subsequent decision-making structure that the \ncommunity adopts. To evaluate the hypothesis that the Ontario community forest \npilot projects (CFPPs) provide enhanced means for public participation, a \ncomparative analysis was undertaken involving a detailed description of decision-making \nstructures of the CFPPs and those of five additional contemporary forest \nmanagement arrangements. The study methods involved: personal interviews \nwith each CFPP?s organizing body, the general public at each CFPP, and \nmanagement personnel at the five cases; and a comprehensive compilation of \ndecision-making structures of all cases based on their documentation. Results of \nthe study indicate that the CFPPs have developed an elaborate public \nparticipation infrastructure that presents the public with more avenues for \nparticipation than any of the other cases included in the study. \nRecommendations are made on procedures of public participation in community \nforest decision-making as well as suggested criteria for evaluating what is \nsuccessful public participation in a community forest.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".