Bibliographic record
Abstract
Forestry educators face many problems today, with these being manifested through declining enrollments. Surveys of forestry graduates from the University of British Columbia, the biggest forestry school in Canada and one of the biggest in North America reveal a complex situation. Students are generally satisfied with the education they receive, and most go on to earn above‐average salaries. However, a significant number remain unemployed two years after graduation, although this may reflect the quality of the individuals more than a saturated job market. Changes in the nature of forestry suggest that current forestry programs need careful evaluation. There appears to be over‐capacity in the education sector, yet there is a marked need for continuing education (although this is not recognized by those most in need of it). In British Columbia, a complete overhaul of the system is needed to ensure better coordination between schools, colleges and universities, continuing education and extension. Any such reorganization will need to look at research funding and delivery, since this is integral to university teaching. The desire to see science‐based forest management is to be welcomed and should provide a strong opportunity for scientists. However, to be effective, practising foresters in both government and industry will need to recognize the limits of their current knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".