Educational needs of the canadian solid wood products industry
Bibliographic record
Abstract
To address problems in wood science educational programs, the Department of Wood Science at the University of British Columbia initiated a needs analysis of the Canadian wood industry.This analysis was conducted using a national mail survey as well as more qualitative focus groups.Results indicate that the current content of a university-based wood products educational program requires a shift in emphasis.A need for analytical and managerial skills was indicated as well as additional emphasis on mechanical processing.While basic wood science should remain strong, a deemphasis was needed to provide the time required for program content expansion.The length of an undergraduate program should be expanded to incorporate an additional year of industry placement as part of the educational process.In addition, a professional master's program should be available to train graduates with non-wood science degrees to bridge into wood product careers.By starting with a blank piece of paper and addressing one (out of many) key groups who hire our graduates, a recognition of the changing needs of the wood products sector is emerging.This is the first step in a process to address many of the previously expressed critical concerns regarding declining enrollments and interest in wood science educational programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".