Labour Force Training and the Relief Workforce at the MinistŠre des Transports du Qu‚bec: Winning Moves
Bibliographic record
Abstract
Labour force training and the relief workforce are major concerns for the Ministere des Transports du Quebec and its partners, particularly within the context of an economy favourable to investment in public infrastructures. Organizations are seeing their fields of jurisdiction become more diversified and new technologies and regulations introduced, forcing them to continuously renew their expertise to deal with new requirements in the area of transportation. As well, in the last few years, the labour market has been affected by the ageing of the labour force, with a rise in the demand for replacements following numerous retirements, while the skilled and qualified labour pool has progressively offered fewer choices in terms of relief workers. This labour shortage affects a number of job classes in the road, rail, maritime and air transportation sectors. Quebec's organizations and companies are accordingly facing ever-greater challenges when it comes to hiring, developing and establishing the loyalty of a qualified workforce. In light of this, the Ministere des Transports has over the last few years implemented various measures to ensure a relief workforce in the scientific and technical areas of transportation, improve resource management, and maintain expertise: actions to encourage youth to enter professions in the field of transportation, a mentoring program, coaching for employees starting in new positions, identification of vulnerable strategic positions, development of skill profiles, training activities, use of new forms of learning, etc. Challenges in terms of training and the relief labour force concern all transportation organizations, whether private or public. While each may have tried to overcome these challenges in its own way and using its own methods, it appears to be increasingly necessary to aim for common strategies better tailored to the scope of the challenges facing us all. All of the measures implemented have had a positive impact not only in terms of human resources, but also on the collaboration between the Ministere and its main partners. For the covering abstract see ITRD E139491.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".