Results of a pilot study to examine the effective integration of apprentices into the industrial construction sector
Bibliographic record
Abstract
The industrial construction sector in Alberta has recently undergone a period of rapid growth, resulting in a shortage of skilled workers in almost all of the major industrial sector trades. To meet these shortages, a need has arisen to increase the utilization of apprentices on industrial construction projects. In an effort to address this issue, the Construction Owners Association of Alberta has established a goal of developing an industry Best Practice on how to improve the on-the-job portion of apprenticeship training and identify means by which the industry can more effectively use apprentices. The challenge is to increase their usage in a cost-effective manner that also provides apprentices with adequate training opportunities. Before this can be done, the impacts and benefits to the various parties involved in industrial construction must be quantified and assessed. This paper describes the findings of a pilot study that was conducted on a major industrial project to help in quantifying the impact of the use of apprentices in the industrial construction sector and to identify methods of effectively increasing their use while simultaneously enhancing their on-the-job learning experience. The main conclusion of this paper is that apprentices can be effectively incorporated in industrial construction, and they can be both productive and cost-effective, provided they are given adequate instruction and supervision. The lessons learned from the study are discussed to provide insight into conducting future studies. Recommendations for an industry Best Practice on the effective utilization of apprentices are presented.Key words: apprentice, electrician, industrial construction, journeyman, labour force, pipefitter, productivity, training, work sampling.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".