Examining the Virtual Leadership of Leaders in Higher Education During the COVID-19 Pandemic: A Case Study
Bibliographic record
Abstract
During the COVID-19 pandemic, student services leaders needed to adapt to working entirely virtually, find creative solutions to adjust their service delivery, and change how they engaged with their teams. Before COVID-19, studies of virtual leadership focused on virtual teams developed because of the geographical distance between team members. This qualitative study investigated virtual leadership and virtual teams developed because of the move to virtual work during the COVID-19 pandemic. It examined how student services leaders changed their leadership behaviours in response to moving to the virtual work environment. Using data gathered through a questionnaire, interviews, and documentation, four themes reflecting how leaders changed their behaviours and practices were identified: reimagining communication, reconstructing work using technology, reframing team support, and reorienting toward hybrid work. Understanding how these leaders managed their virtual teams at a midsized university in southern Ontario provides insight into what practices might be helpful for teams that continue exclusively virtual work and those that transition to a hybrid work approach.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".