Upward Mobility in Education: The Role of Personal Networks Across the Life Course
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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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".