Advancing the role of communications, education, and capacity building in the future of forestry: Communities of practice and community-based learning
Bibliographic record
Abstract
The evolution from tree- and stand-level prescriptions over a rotation to estate- and watershed-level plans over many generations requires individuals and teams to understand and apply scientific, indigenous, and experiential knowledge to address complex issues. The solution must achieve the business and landscape objectives and stand up to public scrutiny while being both practical and cost effective. Communication, education, and capacity building at a community level are critical to defining forestry solutions. Once a discipline only for professional foresters, forestry is now a community of practice represented by forestry professionals. This community includes�but is not limited to�foresters, engineers, biologists, ecologists, technologists, indigenous knowledge keepers, hydrologists, geologists, and geomorphologists as well as economists and social scientists. Forestry professionals must be able to practically apply knowledge acquired through institutional training and education, as well as knowledge and skills acquired through practice and experience. They must be able to reach out to the knowledge sector when faced with unknowns. The knowledge sector must be able to ethically respond as a community of practice to the demands for new science and continuous community-based learning. This paper investigates the role of the knowledge sector in contributing to communications, education, and capacity building for forestry professionals as well as forest-based communities. The concept of ethical commercialization of knowledge and social capital is also introduced.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".