Social contracts and community forestry: how can we design policies and tenure arrangements to generate local benefits in the forestry sector?
Bibliographic record
Abstract
I examine the forest tenure system in British Columbia and evaluate recent attempts to create community-based forest tenures in a broader context of industrial forestry. I focus on whether community forests provide more local benefits compared to various other industrial tenure arrangements, and assess how indicators of local benefits have been affected by major changes in policy instituted in the 2003 Forest Revitalization Plan. Results demonstrate that at a large regional scale, the policy changes were not a large perturbation to indicators of local benefits. Additionally, although community forests do not necessarily meet all expectations in every community, taken as a group, they performed equal to or better than other types of tenures as measured by indicators of local benefits. However, large variation among individual community forests is evident, highlighting the disparate strategies used by communities to promote local benefits and the influence of market forces in the forestry sector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".