An Intersectional Analysis of the Female Postsecondary Advantage: Gender, race, and College Selectivity
Notice bibliographique
Résumé
This dissertation utilizes nationally representative data from the Education Longitudinal Study of 2002 (ELS:2002) to investigate two sets of research questions: 1) Among those who have completed high school or received a GED, to what extent do school behaviors, attitudes toward school/teacher and parental involvement impact gender and racial/ethnic differences in precollege achievement?; 2) Among those who have completed high school or received a GED, to what extent are gender and racial/ethnic differences explained by students' pre-college academic achievement, educational and parental expectations net of socio-economic background, family structure and high school characteristics? Two theoretical perspectives are used: gender role theory and oppositional culture. The results suggest that gender role theory partially explains why females who have completed high school have higher precollege achievement especially high school GPA. It finds that females are better classroom citizens and have more positive school values compared to males. They also have higher educational and parental expectations. Regardless of race/ethnicity, among those who complete high school or the equivalent, females outperform males in high school GPA. The results suggest that oppositional culture does not account for racial/ethnic differences in precollege achievement or college enrollment especially because black students who complete high school do not have lower educational expectations. Also, racial/ethnic differences in school behaviors and attitudes do not explain why black and Hispanic students have lower academic achievement in this population. In terms of race/ethnicity in precollege achievement, among those who have completed high school, white students have great advantages over both black and Hispanic students. The study largely supports structural explanations related to SES and high school characteristics. In terms of college enrollment, high school GPA is the most important factor. Among those who have completed high school, females have a great advantage over males in overall college enrollment and the female advantage also exists within each racial/ethnic group. However, the advantage of females in both selective and nonselective 4-year colleges is explained by gender differences in high school GPA. Among high school graduates, once males are similar to females in terms of high school GPA, these two groups are not different in 4-year college enrollment. For racial/ethnic differences in college enrollment, the study finds that black and Hispanic students have the lowest rate of college enrollment, and they are less likely to go to any college compared to their white peers. However, there is a net black/Hispanic advantage in overall college enrollment, suggesting that when black, Hispanic, and white students have completed high school and have similar socioeconomic background and precollege achievement, black and Hispanic students are more likely than are white students to go to any colleges and especially selective colleges. As was the case for pre-college achievement, the results largely support structural explanations when it comes to racial/ethnic differences in college enrollment.
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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 source (Gemma direct ou Codex distillé), 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 ».