Financial Stress and Health Considerations: A Tradeoff in the Reopening Decisions of U.S. Liberal Arts Colleges during the COVID-19 Pandemic
Bibliographic record
Abstract
This paper presents empirical evidence on factors influencing choices made by members of the Annapolis Group of Liberal Arts colleges regarding whether to operate primarily in-person, primarily online or some flexible alternative during the COVID-19 pandemic of 2020. This paper examines the tradeoff between public health risks and financial standing that school administrators faced when deciding reopening plans. Because in-person instruction at colleges and universities had large effects on COVID-19 case rates, it is critical to understand what caused these decisions. We used binary and multinomial probit models to evaluate an original data set of publicly available data as well as data from the College Crisis Initiative. Binary and multinomial choice model estimates suggest that conditional upon the prevailing level of COVID-19 in their county, financially distressed colleges were approximately 20 percentage points more likely to opt for primarily in-person operations than less financially distressed colleges. These choices highlight an important potential tradeoff between public health and financial concerns present in the higher education sector and emphasize the need for public spending to mitigate adverse health outcomes if a similar situation occurs again.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".