International Collaboration on a Sustainable Forest Management OER Online Program – A Case Study
Bibliographic record
Abstract
Forestry education has always had to adapt to global changes and accommodate students and society's needs. To address the issues of the day, forestry education has cultivated human capacity to understand the complexity of ever-changing environments, master resource management technologies, and engage in global issues. Educational technology and online learning are important in providing flexible, accessible, and effective forest education at the rate and scale needed within the forestry sector. The transition during the COVID-19 pandemic further illustrates the role of online learning in worldwide education. In this context, this paper shares a case study from the Sustainable Forest Management Online Program led by the Faculty of Forestry, University of British Columbia (UBC) and Partner Universities. This study shows that appropriately integrating educational technologies into an internationally developed and recognized high-quality curriculum is an effective way to create accessible and affordable forestry education in meeting the demand of evolving societal and environmental conditions.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".