Turning Leadership Upside-Down and Outside-In During the COVID-19 Pandemic
Bibliographic record
Abstract
COVID-19 has led many to question how education and schooling are supposed to continue in a world of uncertainty. This case follows the journey of Mariam, an Ontario elementary school principal, as she pivoted her leadership while transitioning between in-person and virtual schooling during the COVID-19 pandemic. The case narrative describes the events that turned Mariam’s leadership upside-down and required her to adapt to meet the needs of the school community; in the process, however, she forgot about her own needs. Crisis leadership forced her to learn, unlearn, and relearn her beliefs about her leadership choices and purpose. The case includes teaching notes and two activities: (a) using a crisis leadership framework for reflecting on the learning, unlearning, and relearning of education, schooling, and the purpose of educational leadership during a pandemic, and (b) strategies to help leaders refocus from the outside in using self-care. Instructors can use this case in graduate-level leadership courses and for professional development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".