How extension enhances the knowledge and practice of innovative silviculture in British Columbia, Canada
Bibliographic record
Abstract
The pressures facing natural resource sectors have grown in recent decades, especially as they intersect with Indigenous Rights and Title, environmental sustainability, and economic interests. In British Columbia (BC), Canada, forest management and forestry practices have come under significant scrutiny, largely sparked by the public opposition to the harvesting of old-growth forests, increasing severity of wildfires, economic declines in the forest industry, and the impacts of a changing climate. As the pace and scale of these challenges grow, the forest sector must be equipped to innovate and adapt. Here, we contribute our understanding of “how to do extension” in the forest sector and, building on an historical perspective of extension in BC and beyond, offer recommendations for how extension can support innovative silviculture in BC. Extension is a knowledge process that is practiced in five different forms: one-way knowledge sharing, two-way knowledge exchange, participatory exchange, co-produced knowledge generation, and anticipatory knowledge generation. The outcomes of extension include empowering individuals, organizations, and communities to collaborate and connect knowledge and practice to address complex forest-based challenges. Extension in innovative silviculture, and forestry in general, ensures that disconnected knowledge and scientific systems are bridged, providing pathways to help ensure applied research projects fill knowledge gaps for practitioners, and that forest planning and operations meaningfully identify and manage for multiple values.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".