Leadership on Trial : A Manifesto for Leadership Development
Bibliographic record
Abstract
Recent books and articles have analyzed the causes of the global financial and economic crisis of 2007-09. Yet little attention has been paid to the quality of leadership in organizations that were at the epicentre of the storm, were victims of it, avoided it or even prospered from it. In the summer of 2009 a multi-disciplinary group of Ivey faculty decided to look at the leadership dimensions of the recent financial and economic crisis. We started by writing a working paper that laid out our preliminary views. We then engaged more than 300 business, public sector and not-for-profit leaders in small and large groups, as individuals and collectives, to get their reaction to this paper and, more generally, to discuss te role that organizational leadership played before, during and after the crisis. We examined leadership not just in the financial sector but also in many other public and private sector organizations that were affected by the crisis. In a sense, we were putting leadership on trial. Our aim in doing this was not to identify and assign blame. Rather, we examined leadership during this critical period in recent history to learn what we could, and use the learning to improve practice in leadership today and the development of next generation leaders. As we analyzed the role of leadership in this crisis we were faced with one major question: "Would better leadership have made a difference?" Our answer is unequivocal: "Yes!" We recognize that many people could argue it is unfair to criticize leaders whose decisions were based on their knowledge of the situation at the time and which only eventually, with the aid of 20/20 hindsight proved bad. We respect this view but we disagree with it. Some business and public sector leaders predicted better than others the bursting of the housing bubble and financial markets turmoil, positioned their organizations to avoid problems, and coped with them skillfully. Their organizations were not badly damaged by the crisis and some even prospered. Some governments and regulatory agencies' control and monitoring systems were superior to those in the U.S., the U.K., Ireland, Spain, Iceland and other countries that had to bail out their banks and other industries. Our evidence supports the conclusion that these companies, these agencies, these governments and these countries had better leadership. Good leadership mattered then and good leadership will matter in the future. We are presenting our conclusions about what good leadership involves in the form of a public statement of principles - a manifesto that addresses what good leaders do, who they are, and how they can be developed in organizations.
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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 | 0.045 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.029 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".