Getting Innovation Right: How Leaders Leverage Inflection Points to Drive Success
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.
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
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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 it