Succeed On Your Own Terms: Lessons From Top Achievers Around the World on Developing Your Unique Potential
Bibliographic record
Abstract
WHAT MAKES TOP ACHIEVERS SUCCESSFUL? Is it more energy? Luck? Drive? Focus? Vision? These are some of the questions answered in Herb Greenberg and Patrick Sweeney's illuminating book, Succeed on Your Own Terms. Greenberg and Sweeney spent two years traveling in more than two dozen countries interviewing some of the world's most accomplished individuals - including renowned architect Michael Graves; Chief Financial Officer of Dun and Bradstreet, Sara Mathew; former Dallas Cowboy Roger Staubach; legendary civil rights advocate Congressman John Lewis; actor Ben Vereen; Holocaust survivor Samuel Pisar; President of Home Depot Canada, Annette Verschuren; mountain climber Rebecca Stephens; the shortest NBA player of all time, Muggsy Bogues; Senator Barbara Boxer; cancer survivor Janet Lasley; and Philadelphia Eagles owner Jeffrey Lurie. Through in-depth interviews and results from a comprehensive personality assessment, the authors uncover the defining qualities that set each of these remarkable individuals apart. These inspiring individuals exemplify 19 defining qualities that can drive your success, such as * Optimism * Resilience * Empathy * Persuasiveness * Courage * Perseverance * Willingness to Take Risks * Creativity * Competitiveness * Confidence * Self-Awareness And you'll learn how to identify these qualities in yourself by taking a free, in-depth personality assessment that can help you discover your unique potential and strengths. Then you will be poised to seek out situations that play to your natural abilities, recognize your defining moments and seize opportunities to succeed on your own terms.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".