Explaining Wealth-Based Disparities in Higher Education Attendance: The Role of Societal Factors
Bibliographic record
Abstract
This article examines factors associated with wealth-based inequalities in higher education attendance at the national level. We draw on data from 99 countries to calculate two distinct country-level indicators for the extent of wealth-based inequality in higher education attendance, namely the dissimilarity index (D-Index) and the Human Opportunity Index (HOI). We then examine each indicator’s association with country-level factors using a series of regression models. We find that secondary completion rates, national wealth, economic inequality and the extent of political egalitarianism are all associated with wealth-based disparities in higher education access. However, there are important differences between indicators. Economic inequality is associated with disparities in access but not the level of overall access. In contrast, politically egalitarianism is associated with expanded educational access, but not wealth-based disparities alone. The study suggests that both economic and political equality are associated with higher educational outcomes. Yet, it also cautions that how we conceptualize and measure educational equity can shape our interpretations of the extent of a country’s educational equity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".