Access Denied: The Effect of Apprenticeship Restrictions in Skilled Trades
Bibliographic record
Abstract
Skilled trades workers – ranging from electricians to carpenters to welders – are a crucial component of the Canadian labour force. However, many employers report that there are shortages of skilled workers in these occupations. Federal and provincial governments have targeted many grant and tax credit programs to encourage workers to become apprentices in the skilled trades. However, myriad provincial regulations that limit how many apprentices firms may hire are stymieing these efforts and limiting apprenticeship opportunities. Provinces regulate whether workers must complete a certified apprenticeship in order to legally work in an occupation, as well as the length of apprenticeship terms. This Commentary finds that strict provincial regulations on the rate at which firms may hire apprentices, which is relative to the number of certified workers they employ, reduce the number of people who work in a trade. Furthermore, the trades in provinces with the strictest regulations on hiring have lower levels of young workers while workers who manage to find work in these trades have higher incomes, suggesting that these regulations are acting as barriers to entry. Governments have set these regulations in order to protect workers and the general public by encouraging workers to gain the proper training in skilled trades. However, entry restrictions are not the best means by which to regulate the quality and safety of work for all trades. Instead of regulating the rate of apprentice entry, governments should focus on regulating the quality of work and safety standards when appropriate. In other words, instead of regulating inputs governments should shift the focus of trades’ regulation to outputs. With recent moves by the federal government to encourage workers to enter the trades, it is now up to the provinces to eliminate antiquated and harmful regulations on 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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".