Perceived and Ideal Inequality in University Endowments in the United States
Bibliographic record
Abstract
Whether and which university to attend are among the most financially consequential choices most people make. Universities with relatively larger endowments can offer better education experiences, which can drive inequality in students' subsequent outcomes. We first explore three interrelated questions: the current educational inequality across U.S. universities, people's perceptions of this inequality, and their desired inequality. Educational inequality is large: the top 20% of universities have 80% of the total university endowment wealth while the bottom 20% have around 1%. Studies 1 to 3 demonstrated that people underestimate university endowment inequality and desire more equality. These perceptions and ideals were mostly unaffected by contextual factors (e.g., salience of endowment consequences, distribution range) and were not well explained by participants' demographics. Finally, Study 4 revealed that learning about current endowment inequality decreased tolerance of the distribution of university wealth. We discuss the implications of awareness of educational inequality for behaviors and educational policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".