Crucial contributors? Re-examining labour market impact and workplace-training intensity in Canadian trades apprenticeship
Bibliographic record
Abstract
Canadian apprenticeship policy has recently turned to direct subsidies for participants, including a federal tax incentive for employers. Some assumptions underlying the employer subsidy are: that apprenticeship training is a principal contributor to the skilled trades labour supply; that employers of apprentices typically incur high training cost and risks; and that in the absence of offsetting incentives, these would deter their participation. These assumptions are tested, using an analysis of 2006 census data and a series of 33 employer interviews. The census data reveal that, in 74 “skilled trades†occupations (NOC-S group H), the proportion of the labour force reporting an apprenticeship credential is 37%. When certificates granted to “trade qualifiers†are excluded from the total, registered apprenticeship certification is found to contribute roughly 25% of the skilled trades labour supply. A closer examination of the census data reveals strong inter-occupational differences in the certification rate and in the ratio of certified to less-than-certified workers, suggesting a de facto hierarchy of trades occupations. The interviews reveal sharp variations in employers’ workplace training efforts, challenging the twin suppositions that employers of apprentices are uniformly high contributors to skill formation, and that high training-related costs risks generally deter their participation. Differences in training behaviour are attributed to high-skill versus low-skill business strategies that in turn reflect differing product markets and regulatory constraints. Whatever the level of their training effort, all of the participating employers are able to minimize the training-related risks that have been cited as the principal rationale for employer subsidies. The paper argues for a more nuanced approach to skills policy and research in Canada, with greater attention to the diversity of actors’ strategic interactions with the training system.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".