Relative influence of contextual factors on deliberation and development of cooperation in community-based forest management in Ontario, Canada
Bibliographic record
Abstract
Co-management of forests has been reported from more than fifty countries and stakeholder advisory committees have become central to forest planning in Canada. Scientists tend to agree that local users are capable of self-organising to manage resources more effectively than state agencies alone, whether jointly with governments or with considerable autonomy. Little, however, is known about how the network of contextual influences helps, hinders, or overrides the deliberation that facilitates the development of cooperation critical for co-management success. The objectives of the paper are to identify the relative influence of contextual factors, participants’ sense of control over contextual factors, and effects on performance. In a comparative case study of two stakeholder advisory committees in Ontario, Canada, the objectives are addressed by identifying and analysing advisory committee thinking about consensus building using network analysis of group cognitive maps. The paper concludes with three lessons regarding how the mix of hierarchical, market, and community institutions that influence community-based deliberation can be coordinated for effective 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".