Cumulative advantages and disadvantages in attainment of higher education: Set-analytic comparison of asymmetric inequalities in six European countries
Bibliographic record
Abstract
This article explores how parental resources work together to secure higher education for their offspring. It does so by, first, mapping the linkages between cumulative advantages and disadvantages of respondents’ parental resources and educational attainment across countries and cohorts. Second, investigating under which institutional setup of education systems these linkages between parental background and educational attainment are the weakest. At both levels, the set-analytic approach is applied. We show that disadvantages tend to cumulate to a much greater extent than advantages and their role in hindering higher educational attainment is much stronger than advantages to enable it. The only configuration of educational system that is sufficient to mitigate linkages between cumulative background and educational attainment in both directions, that is, advantageous background to enable and disadvantageous background to hinder higher educational attainment, combines high levels of standardization and decommodification.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".