Agility: an essential element of leadership for an evolving educational landscape
Bibliographic record
Abstract
Defined as the ability to think and move quickly and easily, the importance of agility as an essential element in the move forward for leaders of schools and systems postpandemic, as a result of the impact of COVID-19 on children, is examined. The smartness of a leader’s continuous interactions with the multi-faceted features of their environment, the very nature of the ever-evolving educational landscape of today, is of tremendous value for the leadership of tomorrow. Through the prioritization of strategic objectives in balanced measure, connectivity through relationships and partnership building, proactivity for effective change management, ingenuity in the optimization of resources over time, and the cultivation of systemness throughout the organization—as aspects of agility—educational leaders have the bona fide chance of a lifetime to transform school systems in the pursuit of achievement, equity, and well-being for the benefit of all students, staff, and school communities. Additional considerations, including barriers to agility, are also addressed as are recommendations for leaders of schools and systems as they navigate the shifts in organizational terrain caused by the disruption.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".