Youth apprentice engagement in Ontario: what can be learned from Switzerland’s vocational education and training (VET) system?
Bibliographic record
Abstract
Canadian apprenticeship models do not effectively match young people to employment opportunities. The Government of Ontario identified the problem in the 2019 Budget that more apprenticeship initiatives need to encourage young people to join the skilled trades industry because of a high demand (Department of Finance Canada 2019, 46). In contrast, Switzerland's apprenticeship model is considered a best practice because of its success in keeping youth unemployment and student debt low (Embassy of Switzerland 2019). The purpose of this report is to explore whether the Switzerland apprenticeship model would be a best practice for Ontario. This comparative study collected secondary data undertaking a documentary analysis on the social, political and economic events that occurred in Switzerland and Ontario. The findings in this paper are that the Swiss VET can be used as a best practice to shape other training systems to become more innovative and versatile to economic demands. Key Words: Dual System, Swiss Vocational Education and Training, Ontario's Apprenticeship
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".