Getting Innovation Right: How Leaders Leverage Inflection Points to Drive Success
Bibliographic record
Abstract
List of Figures and Tables ix Introduction xi 1 Pursue and Leverage Inflection Points 1 Expert Input: Cindy Hallberlin of Good360.org on Getting Ahead of an Inflection Point 31 2 Build Innovation Capacity 37 Expert Input: Jeanne Tisinger of the Central Intelligence Agency on Building Capacity 44 Expert Input: Paul Pluschkell of Spigit on Idea Management 59 3 Collect Intelligence 65 Expert Input: Ken Garrison of Strategic and Competitive Intelligence Professionals on Competitive Intelligence 86 4 Shift Perspective 93 Expert Input: Roger Martin of the University of Toronto s Joseph L. Rotman School of Management on Thinking Differently 104 5 Exploit Disruption 109 Expert Input: William D. Eggers of Deloitte s Public Leadership Institute on Disruption and Government 124 6 Generate Value 147 Expert Input: Mark Katz of Arent Fox LLP on Generating Value 158 7 Drive Innovation Uptake 183 Expert Input: Mark Hurst of Creative Good on Getting Close to Customers 201 Appendix A: Sample Business Intelligence Contract 219 Appendix B: High-Level Outline of a Typical Business Plan 223 Appendix C: Simplified Business Plan Financial Model 225 Notes 227 Acknowledgments 233 About the Author 235 Index 237
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.017 |
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".