TRANSFOR-M: A unique transatlantic forestry Master program leading to a dual European and Canadian degree
Bibliographic record
Abstract
To educate their students in modern sustainable forest and environmental management approaches sensitive to cultural and situational differences, three Canadian (Alberta, British Columbia, New Brunswick) and four European (Albert-Ludwigs- Universität, Freiburg, Germany; University of Eastern Finland, Joensuu, Finland; Swedish University of Agricultural Sciences, Umeå, Sweden; and Bangor University, Wales) universities have developed a new transatlantic forestry Master program leading to a dual European and Canadian post-graduate degree (TRANSFOR-M). The two-year English language program has the following key characteristics: 1) the optimal use of expertise at partner institutions to deliver effective, globally oriented programs in forestry and environmental management; 2) one intensive language course in the language of the host country for the Canadian students; 3) e-learning courses accessible among all partner institutions (and once tested through TRANSFOR-M, to a broader audience); 4) a “thesis” or research project report that is co-supervised by both a Canadian and a European professor; 5) access to work internships to provide practical experience in an international context and increase the employability of the graduate students and 6) two mandatory three-week field courses (one across the four European countries and one across the three Canadian provinces), where all program participants meet.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".