Apprenticeship Issues and Challenges Facing Canadian Manufacturing Industries
Bibliographic record
Abstract
The apprenticeship system is generally associated with the construction industry. However, the manufacturing industry actually employs a greater amount of persons in apprenticeable occupations than construction. With the rise in value of the Canadian dollar and increased international competition from developing countries, manufacturing industries must increasingly invest in the skills of their workers. Apprenticeship training is often viewed as a possible solution to this challenge. The objective of this report is to discuss issues related to skilled labour shortages and to apprenticeship in manufacturing. The report finds that in recent years the manufacturing sector has suffered from low output and employment growth. In contrast with these findings, the manufacturing sector is reporting increasing shortages of skilled labour. These conflicting indicators suggest that skills shortages in the manufacturing sector are a result of a strong overall labour market rather than dependent on sector specific developments. Growing skills shortages underline the importance for the manufacturing to train and retain employees despite the poor market conditions prevailing in the sector. In this context, apprenticeship programs are highly relevant to the manufacturing sector as 14 per cent of its workforce is in apprenticeable occupations. However, strong growth in the number of apprentices in manufacturing has not been followed by a commensurate increase in the number of completions. Much needs to be done if the apprenticeship system is to significantly foster the international competitiveness of the Canadian manufacturing sector through the development of a highly skilled workforce.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".