Does adult training benefit Canadian workers
Bibliographic record
Abstract
Using longitudinal data for Canada, the probability of participating in employer supported course enrollment for mid career workers and the wage impacts of those adult educational investments are analyzed. Probability of participation in employer supported course enrollment is increasing with age, job tenure and education, and is lower for visible minority workers. Using a parametric difference-in-differences model to minimize the effects of selection into training, we find strong positive effects of employer supported course enrollment on wage changes over time. The estimated effect ranges from 6.8 to 7.7 percent wage growth for men and 7.5 to 9.3 percent wage growth for women. When the linear specification of the outcome equation is relaxed and an empirical common support is implemented through semiparametric difference-in-differences matching methods, the average treatment effect on the treated estimates from the log wage change models were smaller in magnitude than the corresponding parametric estimates but were typically still statistically significant and in the range of 4.2 to 7.6 percent for men and 7.6 to 7.1 percent for women. An analysis of respondents’ health outcomes shows no clear relationship with participation in employer supported course enrollment.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".