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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.781
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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