Gender and job-related non-formal training: A comparison of 20 countries
Bibliographic record
Abstract
This article analyses gender differences in the participation in various types of job-related non-formal training in 20 societies and examines the relationship of these gender differences with country-specific institutional settings such as employment protection, family policies and the gender culture. Using data from the Programme for the International Assessment of Adult Competencies (PIAAC) and applying two-step multilevel regression analyses, two main findings are obtained: First, gendered participation clearly differs among training types, with women being less likely to participate in employer-financed training but more likely to participate in non-employer-sponsored training. These gender differences in training participation are crucial because they are likely to shape men’s and women’s career development in different ways, that is, by providing better future career prospects with the current employer for men and with a new employer for women. Second, country-specific settings can reduce gender differences in training participation: in countries with family policies supporting females’ employment (e.g. good coverage of formal childcare and short parental leave), we found a lower training disadvantage of women in employer-financed training. In turn, gender differences in non-employer-sponsored training seem to be lower in countries with less rigid employment protection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".