Social Capital of Non-traditional Students at a German University. Do Traditional and Non-traditional Students Access Different Social Resources?
Bibliographic record
Abstract
Social capital is of particular value for the acquisition of education. Not only does it prevent scholars from dropping out but it improves the educational achievement. The paper focuses on access to social resources by traditional and non-traditionals students at a Germany University and asks if there are group differences considering this important precondition of academic achievement. We assess students’ access to social capital with an abbreviated and adjusted version of van der Gaag and Snijders’ (2005) Resource Generator. We compare the access to social capital among traditional and non-traditional students and take a close look at the effects of social origin on the availability and structure of social capital. Non-traditional students are a group of students which did not attain a general qualification for university entrance, but instead were accepted for university studies by completing an entrance examination. Before commencing university studies, they often completed an apprenticeship and worked for some years. Because of their different educational careers and living conditions, we expect that non-traditional and traditional students access social capital in different parts of their social networks. Our results indicate that the different educational backgrounds of students impact their access to social capital. However multivariate analyses illustrate that most differences in social capital access can be put down to diverging group compositions. Core determinants of the social capital access are socio-economic background and vocational education: Students from higher socio-economic backgrounds and students who completed vocational education have access to more social capital than their fellow students.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".