Apprenticeship Program Requirements and Apprenticeship Completion Rates in Canada
Bibliographic record
Abstract
Over the past two decades there has been considerable growth in the number of new apprenticeship registrations in Canada. However, this has not been matched by a corresponding increase in the number of apprenticeship completions. As a result Canadian apprenticeship programs have seen declining completion rates over this period. Across provinces, trades and time there is considerable variation in apprenticeship completion rates. In Canada apprenticeship programs are provincially regulated and there are differences in requirements across trades and provinces and, to a lesser extent, over time. Therefore, this paper asks to what extent the diff erences in completion rates are related to diff erences in the structure of apprenticeship programs, as well as di fferences in demographic variables and unemployment rates. Results suggest that apprenticeship programs for which certi cation is mandatory have completion rates that are about ten percentage points higher than those without mandatory certifi cation. There is little evidence to support the view that either the length of the work experience term or the technical training requirement act as a barrier to completion. However, there is some evidence to suggest that the format in which technical training is delivered is related to completion rates. While the decline in completion rates during the 1990s coincided with the raising of education requirements, accounting for the trend in completion rates implies a positive relationship between these two variables across trades and provinces. On average, trades with a higher fraction of female apprentices and apprentices with a younger average age tend to have higher completion rates. Finally, in general the results are consistent with high unemployment rates acting as a barrier to completion.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".