Educational Systems and Gender Segregation in Education: A Three-Country Comparison of Germany, Norway and Canada
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.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".