Flattening the Hierarchy Curve: Adaptive Leadership during the Covid-19 Pandemic – A Case Study in an Academic Teacher Training College
Bibliographic record
Abstract
The Covid-19 pandemic forced institutions of higher education to adopt agile leadership behaviors. The current research aims to examine how the leadership at the Ohalo teacher training college in Israel dealt with the crisis caused by the pandemic. The research hypothesis, predicting a positive relationship between the college leadership’s decisions and lecturers’ positive evaluations regarding these decisions, was confirmed. Previous research has given scant attention to the relationship between running an academic institution and applying principles of adaptive leadership during a crisis. This article presents a case study of adaptive leadership at an academic institution during the Covid-19 pandemic. The conclusions suggest that ensuring the continued functioning of an organization during a crisis requires skills and competencies reflecting multifaceted and adaptive leadership, agility, and direct channels of reciprocal, cooperative communication. Opportunities for initiative taking should be provided, and a consistent policy must be maintained that aims to “flatten the hierarchy curve.”
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".