Limitations of the Community Forest Agreement as a framework for community foresty in British Columbia
Bibliographic record
Abstract
Recent decades have seen a growing interest in community forestry as a way to bridge the transition from a sustained yield paradigm into sustainable forest management. In light of this, community forest agreements (CFAs) were created to offer a tenure option that facilitates the implementation of community forestry province-wide. While this is a step in the right direction and a more adequate arrangement than existing industrial forms of tenure, it is doubtful that this tenure in its current form can fully serve the objectives that it was originally meant to fulfill. CFA holders report a variety of challenges, many of which stem from the CFA tenure structure. Key shortcomings reported across the board include lack of control over non-timber resources, lack of strategic decision-making power and small economies of scale. Any sincere efforts to expand the community forest program require that these limitations be addressed through more appropriate tenure arrangements.
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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 | 0.025 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".