HUMAN RESOURCE NEEDS AND DEMAND FOR POST-SECONDARY EDUCATION IN THE CANADIAN SECONDARY WOOD PRODUCTS INDUSTRY
Bibliographic record
Abstract
Skilled labor shortages in manufacturing industries are being reported in a number of countries. The extent to which such shortages are affecting Canadian wood manufacturing industries is not known. The aim of this study was to survey the skills and educational needs of Canadian wood manufacturing industries, the status, capacity, and challenges that post-secondary education institutes face in meeting industries' human resource needs, and finally the attractiveness of the industry to high school job-seekers. The majority (83%) of wood manufacturing companies in Canada are experiencing problems in hiring skilled tradespeople, and 54.5% of the companies face difficulties in hiring and retaining professionals and supervisory personnel. Skilled staff shortages and competitive pressures were nominated by companies as the two most important factors restricting their growth. The skills needs of companies have changed over the last 10 years, and companies now place a much higher premium on leadership and communication skills from management, and attitude and advanced technical skills from tradespeople. Most companies offered some kind of in-house training to redress skills gaps, but less than half were likely to use the Internet to deliver in-house training. The majority of high school students were not interested in pursuing a career in the wood manufacturing industry because of its association with unsustainable forestry practices and manual labor, and the availability of more attractive career options. Lack of student interest in wood manufacturing is affecting the institutions offering relevant vocational and professional training, and nine of the thirteen institutes suffer from under-enrollment in their wood manufacturing programs. Comprehensive strategies are required to address the labor market imbalances currently affecting Canada's wood manufacturing industries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".