Diversity on Wheels: Retaining Canada’s Immigrant, Young, and Women Truck Drivers
Bibliographic record
Abstract
Transporting goods across Canada’s vast landmass on a daily basis, trucks can be seen as carriers of the country’s prosperity. In the meantime, like many countries across the globe, Canada is currently suffering from a concerning truck driver shortage. Given Canada’s reliance on trucks, addressing this shortage is crucial. My qualitative study explores factors that affect the retention of immigrant, young, and women drivers, who are currently underrepresented as truck drivers, yet who will likely become the backbone of Canada’s trucking industry in light of its changing demographics. Based on the Job Demands-Resources Model, my study investigates the job demands that deter these drivers from staying in the industry and the job resources that encourage them to stay. Key findings highlight job demands including driving related working conditions, perceived discrimination, and exploitation that are critical to address, along with important job resources of financial and non-financial nature that need to be further encouraged to help reduce Canada’s truck driver shortage. My study provides practical recommendations to three levels of stakeholders – government agencies, industrial leaders, and the general public.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".