Knowledge transfer and extension in the Canadian Ecology Centre – Forestry Research Partnership: From awareness to uptake
Bibliographic record
Abstract
Knowledge transfer, including awareness, transfer, extension, training, and education activities, was purposely incorporated into the Canadian Ecology Centre – Forestry Research Partnership (CEC-FRP) at an early stage as part of an adaptive management approach. Over the course of 7 years, the level of involvement from researchers, policy-makers, and forest resource managers in knowledge transfer activities progressed from passive to interactive participation, with each successive stage requiring greater attention to timing and the capacity of participants to take in new knowledge. An interactive approach, dubbed core teams, proved essential in overcoming barriers to the flow of knowledge into practice. Four case examples: (1) revising growth and yield predictions, (2) integrating spatial and nonspatial landscape analysis tools, (3) developing and applying advanced silvicultural decision-making, and (4) applying spray delivery systems, are used to convey the success of knowledge transfer and extension efforts in the CEC-FRP and the essential role of the core teams. Physical, human, and financial resources, coupled with strong involvement by partner organizations, were key factors in the success of knowledge transfer efforts. Key words: active adaptive management, forest management
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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 | 0.042 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".