Canadian Apprenticeship and Effect of Union Membership Status 15
Bibliographic record
Abstract
Abstract: This study examines structural, organizational and demographic characteristics and union membership status as they affect participation in different types of informal learning and formal training, including a special focus on apprenticeship. Results presented in this study are based on analysis of the data from the Apprenticeship Information System, the 2004 WALL Survey, and the 2003, 1997, and 1993 AETS surveys. This study demonstrates that membership in a trade union has a significant impact on employees ’ level of participation in both formal education and informal learning. The study also shows that unionized workers are 25 % to 89 % more likely to participate in registered apprenticeship training than their non-unionized counterparts. The results, based on the most recent data on registered apprenticeship participation in Canada, show significant variation but generally an increased number of employees enrolled in apprenticeship training. This study also shows increased female participation in apprenticeship training but their enrolment is still ten times lower than their male counterparts. This study proposes a number of measures for accurate monitoring and informed decision-making on registered apprenticeship training enrolment, completion and, most importantly, the working and living conditions that trainees encounter throughout their demanding work and learning activities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".