Effectiveness of dispute resolution mechanisms in natural resource management in Ontario
Bibliographic record
Abstract
For a variety of reasons, the number and complexity of natural resource management \nconflicts in Ontario is increasing rapidly. To evaluate the premise that existing natural \nresource management planning processes are ineffective in preventing and/or resolving \nnatural resource use and management disputes, fifteen cases of natural resource conflict \nprevention/resolution processes in Ontario were studied. The public \nconsultation/participation guidelines outlined in the Ministry of Natural Resources? Timber \nManagement Planning Manual for Crown Lands in Ontario (OMNR 1986a) were used as one \nof the cases and as a benchmark for the comparison of other cases. Based on the analyses, \ncharacteristics, pros, cons and effectiveness of each prevention/resolution process studied \nwere described. Conclusions support the premise and suggest that effective conflict \nprevention/resolution processes must have the ability to be modified to accommodate unique \nconflict characteristics such as type and source of conflict. Processes which allow for \nvarying levels of public participation and consultation tend to be most adaptable to necessary \nmodifications.
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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 | 0.019 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".