Gender and job-related non-formal training: A comparison of 20 countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it