From pandemic crisis to recovery and resilience: lessons from COVID-19 at a large urban research university
Bibliographic record
Abstract
The abrupt onset of the COVID-19 pandemic forced a dramatic shift in higher education. Over time, the prolonged and cyclical nature of public-health restrictions conditioned students, faculty, and staff to adopt a crisis mindset as their baseline. Moving from crisis to recovery therefore posed unique obstacles at both individual (e.g. anxiety, exhaustion, and post-traumatic stress) and organizational levels (e.g. transition logistics, labor market changes, and student preparation). Using case study methodology, this paper describes an effort to directly address the evolution from pandemic crisis to recovery and future resilience at large, urban, research-intensive university spanning three campuses. Consultation meetings in the form of individual interviews and focus groups with 301 academic leaders, staff leaders, and student leaders across the institution raised critical insights into the process of adapting to change in an institution of higher learning. The analysis of discoveries and resulting actions clustered into four themes: fatigue, loss, and pride in the aftermath of crisis; moving forward (including recognizing efforts and challenges to integration); innovation out of adversity caused by COVID-19; and future-proofing by seizing opportunities for creating resilience. Despite the chaos that crises may introduce, this case study illustrates how they carry unique opportunities for growth. As the future will continue to present all manner of challenges, the willingness and ability to adapt will define future outcomes for higher education.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".