Vertikale und horizontale Ungleichheiten am Übergang in die tertiäre Bildung - Unterschiede nach dem Migrationshintergrund und der sozialen Herkunft -
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».