Educational Systems and Gender Segregation in Education: A Three-Country Comparison of Germany, Norway and Canada
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
Abstract How do institutional settings and their embedded policy principles affect gender-typed enrolment in educational programmes? Based on gender-sensitive theories on career choice, we hypothesised that gender segregation in education is higher with a wider range of offers of vocational programmes. By analysing youth survey and panel data, we tested this assumption for Germany, Norway and Canada, three countries whose educational systems represent a different mix of academic, vocational and universalistic education principles. We found that vocational programmes are considerably more gender-segregated than are academic (e.g. university) programmes. Men, more so than women, can avoid gender-typed programmes by passing on to a university education. This in turn means that as long as their secondary school achievement does not allow for a higher education career, they have a higher likelihood of being allocated to male-typed programmes in the vocational education and training (VET) system. In addition, social background and the age at which students have to choose educational offers impact on the transition to gendered educational programmes. Overall, gender segregation in education is highest in Germany and the lowest in Canada. We interpret the differences between these countries with respect to the constellations of educational principles and policies in the respective countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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