Governance and management of small forest tenures in British Columbia
Bibliographic record
Abstract
The growing number of small tenures in British Columbia creates new demands on local organizations to manage public forest lands. To deal with these demands, small tenure holders must develop governance practices that address both accountability and participation. Local participation is especially important for Community Forest Agreement (CFA) holders to ensure that community members are actively involved in decision-making processes. Both cfas and Woodlot licensees have upward accountability to the B.C. Ministry of Forests and Range. Holders of CFAs also have downward accountability to members of the local community. Community forests in the province have adopted various legal structures. Private corporations owned by local government are popular vehicles to hold CFAs and have commercial advantages, but their structure is less accountable than others. Although it is important to separate the political decisions of community forest governance from the technical decisions of management, both are needed. Experience with small tenures in other countries suggests that scope exists for sustainable, commercial forest management based on a substantial degree of local autonomy, if accompanied by technical support and oversight from governments, as well as training, extension, and services from voluntary associations of local tenure holders. Further study of options and experience with local forest governance and management will be helpful for small tenures in British Columbia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".