Social contracts and community forestry: how can we design forest policies and tenure arrangements to generate local benefits?
Bibliographic record
Abstract
There is widespread debate about the best strategies to provide local benefits in forest management. We evaluate recent policy changes in British Columbia, Canada, focussing on attempts to create local benefits from public forests through a community forestry program and broad policy changes in 2003 that removed obligations of tenure holders to process timber in areas near where timber was harvested. These obligations were intended to retain benefits of milling jobs locally and were considered part of a “social contract”. We evaluate these policy changes by asking two specific questions. (1) Do community forest tenures provide more local benefits than major industrial tenures? (2) How have the policy changes of 2003 affected the patterns of fibre flow over the period to 2008? We evaluate these questions through qualitative research and a quantitative fibre flow analysis using a large time-series dataset. Community forests as a group performed better than major industrial tenures in delivering local benefits as we defined them. However, large variation among individual community forests is evident, highlighting the disparate strategies used by communities to promote local benefits. Our fibre flow analysis did not reveal major changes following 2003, suggesting that broader fibre flow trends mask more local perturbations.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".