Holding onto the victory after the victory: Leadership lessons from the war in Ukraine for recovery and positive change
Bibliographic record
Abstract
Case studies of countries at war are not typically part of the curriculum at business schools. However, such events have lots to offer in terms of leadership. Consider the following: Analysts estimated that the Russian Armed Forces would be capable of capturing Kyiv and removing the Ukrainian government within three days after the start of the full-scale invasion. However, more than three years into the war, Ukrainians have defied this prediction and continue to live through unimaginable hardship with exceptional fortitude. We highlight five main themes from the ongoing war and associated lessons for educational institutions, businesses, and leaders – resilience; fragmentation; grief; critical thinking; and vision – and make the point to never forget about the victory after the victory. Holding onto the victory will be crucial, not only to find a way forward but to keep despair at bay. Purpose, new perspective, and a sense of contributing to something larger can grow out of the need for wartime resilience. A new awareness of and commitment to gender equity can arise from fragmentation. Loss and grief can motivate societal change through post-traumatic growth. And the horrors of war can also serve as a wake-up call that leads to increased education and critical thinking. Such transformation is ongoing and can start now, even before the war is over.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".