Tales From Three Countries and One Academia: Academic Faculty in the Time of the Pandemic
Bibliographic record
Abstract
Since the start of the pandemic and the related major disruptions introduced to all aspects of university work and life, one invariable focus has been on students and the effects of dislocation, lockdown, illness, and isolation, not only on their academic performance and career advancement but also on their physical well-being and mental health. In a departure from this focus, this editorial turns attention to faculty members and analyzes the different paths and management strategies that universities around the globe took through the pandemic. Bringing perspectives from Australia, Canada, and the United States, from online-only, on-campus only, and hybrid programs, this editorial highlights the gamut of experiences with and views on navigating the pandemic in higher education from the faculty perspective. Some re-flections are personal and program-bound (Garner, Thompson), whereas others are analytical and span the entire academic realm (Caidi, Dali). We introduce a series of vignettes that, together, compose a picture of faculty struggles and triumphs during the pandemic. We also touch on the administrative and managerial context in which the events of 2020–21 in academia unfolded. We call these vignettes “tales.” These tales address the differences and striking commonalities in experiences of faculty across institutions and geographic borders, pointing to new challenges, successful innovations, and surprising constants—positive and negative—that came to the fore and were accentuated during the time of crisis and change.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.052 | 0.023 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".