International Collaboration on a Sustainable Forestry Management OER Online Program – A Case Study
Bibliographic record
Abstract
Over time, forest education has had to adapt to keep up with global changes and to accomodate the needs of students and society. While facing pressing global issues like climate change, deforestation, illegal logging and food security, the role of higher forest education has shifted away from traditional teaching approaches and practices to methods that emphasize sustainable development, community-based management and environmental conservation in forestry. In doing so, forest education has cultivated human expertise that understands the complexity of ever-changing environments, masters state of the art technologies to manage fores and natural resources, and is capable of creating, communicating and implementing related policies in global communities and societies. In this context, educational technology and online lerning enable flexible, accessible, effective, and high-quality forest education. A case study of a Sustainable Forest Management Online program led by the Faculty of Forestry, University of British Columbia (UBC) shows that appropriately integrating educational technologies into an interntionally developed and recognized high quality curriculum is an effective way to create accessible and affordable forest education in meeting the demand of evolving societal and environmental conditions.Keywords: forest education; educational technology; international collaboration, open educational resources
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".