Aiming high: social and academic correlates of applying to and attending ‘reach’ universities
Bibliographic record
Abstract
Less privileged students disproportionately aim low with their university applications and often fail to apply to the institutions that match their academic qualifications. Little attention, however, has been directed toward the converse phenomenon, that of aiming high. These kinds of applications are commonly called ‘reaches,’ meaning an applicant’s credentials fall slightly below the institution’s range for the average first-year student. Using nationally representative survey data from the United States, this study examines whether social background predicts the likelihood of applying to reach institutions and assesses the consequences for disparities in enrollments. We find a strong relationship between students’ socioeconomic background and their likelihood of applying to reach institutions, even after controlling for a range of academic and non-academic characteristics. Further, there is a substantial pay-off to applying to reach institutions, revealing the degree to which these class-based choices at the application stage contribute to the social stratification of higher education.
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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 | 0.000 | 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".