Rethinking forest governance during a "second war" in British Columbia's woods: a Collaborative Action Framework
Bibliographic record
Abstract
There are persistent conflicts in British Columbia over logging in old growth forests. This study traces the development of a Collaborative Action Framework (CAF) process during the Fairy Creek forest conflict in British Columbia in 2020–2021. The CAF process involved a twoday summit, where more than 80 participants from First Nations, industry, academia, unions, government, and nongovernment organisations developed a vision for the province's forests to 2070. During the summit, six working groups were established to carry the work forward, and build strategies for achieving the 2070 vision. This paper focuses on the Forest Governance Working Group (FGWG), which was chaired by a First Nations leader. This study presents the design and outputs of the CAF and FGWG processes, which sought to inform forest policy and mitigate the risk for other forest conflicts in the province. These processes did not occur in a vacuum, and both inspired and were shaped by a multitude of forest co-governance and power sharing initiatives between First Nations and the government across the province.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".