Social selectivity and gender-segregation across fields of study: Comparative evidence from Austria
Bibliographic record
Abstract
This study explores stratification within the Austrian university system by focusing on social selectivity and gender-segregation across fields of study. We investigate how much the choice of field of study is associated with parental educational background and the gender of the students—especially, how these characteristics vary across individual (teaching) subjects. Teacher training is often regarded as typically chosen by women and preferred by so-called educational climbers. However, previous studies focus on clusters of fields of study and do not take into account the differences between individual (teaching) subjects. We address this research gap by focusing on a comparison between those who have chosen to undergo a teaching program in a specific subject and those who have studied this specific subject without pedagogical training. By using administrative data from first-year students at Austrian state universities ( N = 23,400) in 2016–2017, and applying logistic regression analysis, the results demonstrate that in almost all analyzed fields of study, similar patterns of gender-segregation according to the choice of fields of study can be observed, regardless of whether it concerns a teacher training subject or a corresponding equivalent academic subject. Educational climbers tend to opt more frequently for teacher training subjects than for their corresponding fields—especially in some of the mathematics-oriented science, technology, engineering, and mathematics (STEM) subjects. We contribute to comparative sociological literature by introducing the approach of comparing teacher training subjects to their academic equivalents and revealing a more nuanced picture regarding horizontal inequalities in higher education.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".