Vertikale und horizontale Ungleichheiten am Übergang in die tertiäre Bildung - Unterschiede nach dem Migrationshintergrund und der sozialen Herkunft -
Bibliographic record
Abstract
Social and migration-specific differences at the transition from school to higher education or vocational training as well as within post-school educational pathways form the starting point of this publication-based dissertation. Using data from DZHW Panel Study of School Leavers with a Higher Education Entrance Qualification, four articles examine how group-specific differences in the decision for or against studying occur and can be explained by performance-based as well as decision-based effects (Boudon 1974). Both, the choice of vocational training considering occupational segmentation according to school education, and migration-specific differences in the choice of field of study are subjected to initial empirical tests. The first article addresses the question how differences at the transition to tertiary education due to social origin can be explained. Employing logistic regression and decomposition analysis, it investigates the explanatory contribution of the various mechanisms identified at the theoretical level to the social differences in the likelihood of studying. The results show that the differences are mainly mediated by different decision-making behavior – almost a quarter is due to socially divergent cost estimates. Taking into account all theoretically derived mechanisms, the social differences can be completely "explained away". Since not all students take up studies, the second article considers the aspect of the choice of vocational training. It examines which factors play a role in the aspiration for an occupation that is atypical for those with university entrance qualifications. Using logistic regression analyses, the significance of the students' self-assessed strengths and career goals is highlighted. Contrary to theoretical considerations, female students with an academic background more often pursue occupations that are atypical for their school-leaving qualifications than female students without an academic background. The third article is centred around migration-specific differences at the transition to higher education. It represents a replication and analytical extension of the findings of Kristen, Reimer, and Kogan (2008). The logistic regression analyses show that the findings of Kristen et al. (2008) can be confirmed with more recent data and an appropriate operationalization of the migration background. On average, students with a migration background more often decide to take up studies compared to students without a migration background. However, even when controlling for all theoretically derived factors, the question of how these differences can be explained remains unresolved. The fourth and final article focuses on the choice of a field of study. It questions whether the high educational aspirations of students with a migration background carries on in this decision. The multinomial logistic regressions show that low-prestige subject groups are chosen less often by students with a migration background compared to students without a migration background. Moreover, students of Turkish origin are more likely than others to study a prestigious subject. The latter can be partly explained by the immigrant-optimism-hypothesis.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".