Better under pressure: how great leaders bring out the best in themselves and others
Bibliographic record
Abstract
Most business leaders can take only so much pressure before their performance slides. Yet some CEOs deliver their greatest successes when times get toughest--when customers' preferences are shifting away from a company's products, when new regulations are shrinking profit margins, when political unrest is destroying supply lines. In Better Under Pressure, Justin Menkes reveals the common traits that make these leaders successful. Drawing on in-depth interviews with sixty CEOs from an array of industries and performance data from two hundred other leaders, Menkes shows that great executives strive relentlessly to maximize their own potential--as well as stoke their people's innate thirst for their own triumphs. To do so, they draw on a set of three essential and rare attributes: * Realistic optimism: They recognize the risks threatening their organization's survival--and their own failings--while remaining confident in their ability to have an impact. * Subservience to purpose: They dedicate themselves to pursuing a noble cause and win their team's commitment to that cause. * Finding order in chaos: They find clarity amid the many variables affecting their business by culling data and forming the conclusions that matter most to the company. The good news: these three capabilities can be learned. Drawing on a broad range of examples from real companies--including Avon, Yum Brands, Southwest, Procter & Gamble, and Ryerson Steel, to name just a few--Menkes demonstrates how each psychological attribute manifests itself in real life and enables top performance under extreme duress. He also shows you how to develop and deploy those attributes--so you can transform yourself into a leader who only shines brighter as the pressure intensifies. Deeply personal, brimming with compelling stories from real-life CEOs, and packed with powerful insights, tools, and practices, this book is a potent resource for aspiring, emerging, and seasoned business leaders alike.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".