Decomposing Heterogeneity in Inequality of Educational Opportunities: Family Income and Academic Performance in Brazilian Higher Education
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
Access to higher education depends on the interaction between social origins and academic performance: background resources boost academic skills; but even when controlling for performance, privileged students are more likely to make ambitious choices and further transitions. Recent literature has shown that inequality in educational choices is heterogeneous across countries. However, it is still not well understood how different institutional designs within countries may affect the workings of those effects and how they can strengthen or weaken the inequality of educational opportunities. Using high-quality register data from the Brazilian higher education system, our work contributes to this understanding by investigating how SES and performance interact and drive students' choice between three different tracks: not entering higher education, entering the private system, or entering the public system. We developed a strategy to encompass multinomial choices and decompose the inequalities into primary and secondary effects. Using the Shapley Value decomposition strategy, we correct an intrinsic asymmetry that biased previous results. Our findings suggest affluent students enjoy dual advantages: high exam performance amplifies access to public universities (indirect effect) and family resources offset subpar performance, ensuring private university access (direct effect). We found no signs of multiplicative advantages.
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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 | 0.000 | 0.002 |
| 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 it