Trends in Postsecondary Enrollment During the COVID-19 Pandemic: A Research Note
Bibliographic record
Abstract
The COVID-19 pandemic disrupted nearly every aspect of economic and social life in the United States, especially education. This research note draws on student-level administrative data from one U.S. state to describe how trends in postsecondary enrollment changed during the pandemic. First, students were less likely to enroll in postsecondary institutions following high school graduation during the pandemic, and these declines were most prominent among lower income, Hispanic, and Black students. Second, rates of sustained enrollment in both the immediate year following high school graduation and the next year fell more substantially among lower income, Hispanic, and Black students during the pandemic than they did among higher income and White students. Third, students made different decisions about where to enroll: higher income, White, and Asian students increased their enrollment in public four-year schools, decreased their enrollment in private four-year schools, and were more likely to attend college in-state, whereas lower income, Black, and Hispanic students experienced broad declines across institutional sectors and locations. These results paint a picture of growing socioeconomic and racial and ethnic inequalities in whether and where students pursued postsecondary education, highlighting the unequal barriers placed on traditionally underserved high school graduates during the pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".