Older Workers and On-the-Job Training in Canada: Evidence from the WES Data
Bibliographic record
Abstract
This paper provides evidence of on-the-job training among older workers in Canada. It also examines the effect of age associated with on-the-job training. Statistics Canada’s Workplace and Employee Survey (WES) 2001 data, linking employee responses to workplace (i.e. employer) responses are used. Three quarters of workers are categorized as middle aged, with about one in ten being younger and one in five considered to be older. Only 32% of Canadian workers received on-the-job training in the year preceding this survey. When separating workers into the three age categories, 37%, 34%, and 24% of younger, middle-aged, and older workers, respectively, received on-the-job training in that year. Logistic regression analysis results showed that, controlling for workplace, job and individual factors, as compared to middle-aged workers, older workers are significantly less likely to receive on-the-job training. The lack of on-the-job training for older workers should be a concern for policy makers at a time when labour shortages are being predicted. Older workers are healthier than ever and the provision of on-the-job training should be encouraged to retain older workers in the labour market in Canada.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".