Addressing challenges to recovery and building future resilience in the wake of COVID-19
Bibliographic record
Abstract
While organisational crisis theory posits a predictable set of stages involving pre-planning and preparation, acute crisis response, adaptation and recovery, the prolonged and cyclical nature of public-health restrictions related to COVID-19 presented new challenges for institutions of higher education and conditioned students, faculty and staff to adopt a crisis mindset as their baseline. Consequently, moving from crisis to recovery posed unique obstacles at both individual (eg anxiety, exhaustion and post-traumatic stress) and organisational levels (eg transition logistics, labour market changes and student preparation). This paper describes an effort at a large, urban, research-intensive university to directly address the evolution from pandemic crisis to recovery and future resilience. The University Resilience Project recruited a team of senior staff charged with identifying and adopting promising practices created during the pandemic and decommissioning or archiving less useful policies, procedures and activities, with a view to strengthening the university's resilience. Over the course of more than 300 meetings with academic leaders, staff leaders and student leaders, team members created a space to share the experiences of COVID-19, reflect on successes and challenges over the crisis, and identify opportunities to enhance the resilience of the university. This work raised critical insights into the process of adapting to change in an institution of higher learning.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".