The Unlearning Imperative in Educational Leadership: Recognizing the Need and Navigating the Process
Bibliographic record
Abstract
The evolving landscape of education demands leaders who can adapt and innovate to meet unprecedented challenges. A critical, yet underexplored, competency in this realm is the ability to unlearn outdated leadership paradigms and relearn new, contextually relevant approaches. This extended abstract examines the imperative of unlearning in educational leadership, focusing on how leaders can identify obsolete competencies, discard limiting frameworks, and navigate the complex process of acquiring novel knowledge. Drawing from qualitative and theoretical insights (Hedberg, 1981; Zahra et al., 2021), the study highlights the interplay between unlearning and leadership development within Lithuanian educational institutions. Anchored in the principles of adult learning and andragogy, this research delves into the psychological and organizational dynamics that influence the unlearning process. It identifies key triggers for unlearning, such as institutional reforms and societal shifts, and offers a conceptual model to guide leaders through this transformative journey (Grisold et al., 2017; McGill, 2022). By fostering adaptability and cognitive flexibility, unlearning emerges as a strategic tool to cultivate innovative, responsive leadership in the educational sector. This study not only contributes to theoretical discourse but also provides actionable insights for practitioners aiming to reshape their leadership competencies in a dynamic environment.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| 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".