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Record W615327801 · doi:10.5860/choice.49-2169

Better under pressure: how great leaders bring out the best in themselves and others

2011· article· en· W615327801 on OpenAlexaboutno aff

Bibliographic record

VenueChoice Reviews Online · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicLeadership, Behavior, and Decision-Making Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryAestheticsArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.015
Scholarly communication0.0220.018
Open science0.0010.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.005

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.

Opus teacher head0.496
GPT teacher head0.445
Teacher spread0.050 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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