Gender Segregation in Vocational Education
Bibliographic record
Abstract
Gender inequalities at labor market entry : a comparative view from the eduLIFE project / Moris Triventi ... [et al.] -- Vocational training and gender segregation across Europe / Emer Smyth, Stephanie Steinmetz -- Educational systems and gender segregation in education : a three-country comparison of Germany, Norway and Canada / Christian Imdorf ... [et al.] -- Gender segregation in occupational expectations and in the labour market : international variation and the role of education and training systems / Steffen Hillmert -- Regional gender differences in vocational education in Bulgaria / Petya Ilieva-Trichkova, Rumiana Stoilova, Pepka Boyadjieva -- Explaining the dynamics of occupational segregation by gender : a longitudinal study of the German vocational training system of skilled crafts / Katarzyna Haverkamp, Petrik Runst -- The need for social approval and the choice of gender-typed occupations / Verena Eberhard, Stephanie Matthes, Joachim Gerd Ulrich -- Two sides of the same coin? Applied and general higher education gender stratification in Canada / Ashley Pullman, Lesley Andres -- Gender segregation in Australian science education : contrasting post-secondary VET with university / Joanna Sikora
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".