Upward Mobility in Education: The Role of Personal Networks Across the Life Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do individuals achieve upward mobility in education despite the well‐documented mechanisms that foster reproduction of inequalities? This question presents a fundamental puzzle for social science researchers and has generated an increasing body of research. The present article tackles the puzzle using a life course and personal network lens. Studying educational trajectories in Germany of students whose parents have low educational degrees, it asks: What paths did students take through the education system, what personal network factors were important for their educational attainment, and how did these factors change over students’ life courses? In contrast to most studies that zoom in on a specific transition or time period, the article uses data from 36 retrospective in‐depth interviews that allow a sweeping view of respondents’ educational careers. Thanks to a systematic case selection scheme, the data also enables comparisons between students who became upwardly mobile and those who replicated their parents’ low educational degrees. Findings suggest four types of trajectories: direct upward mobility, indirect upward mobility, direct non‐mobility, and indirect non‐mobility. I discuss four personal network factors that seem to drive these trajectories: support with academic efforts, encouragement, support with solving problems, and role models. Upwardly mobile students showed combinations of two or more of these four factors that established higher education as the students’ goal, and provided them with tools and support to reach that goal. With these findings, the article contributes to literature on inclusion in education, social inequality and mobility, personal networks, and the life course.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it