Wealth-Based Inequalities in Higher Education Attendance: A Global Snapshot
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
This study provides a comprehensive global snapshot of wealth-based inequalities in higher education attendance. We draw on data from 117 countries to describe cross-national patterns in higher education attendance rates, disaggregated by wealth quintile and country income group. We then calculate four different indicators to quantify the size of wealth-based inequality in higher education attendance and completion for each country. Our findings point to large wealth-based inequalities in higher education attendance cross-nationally, which are: substantially larger than inequalities in secondary completion, larger in low- and middle-income countries than high-income countries, and negatively associated with national wealth. The results serve as a foundation for future studies on how country-level factors and policies exacerbate or reduce wealth-based inequalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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