Creating great choices: a leader's guide to integrative thinking
Bibliographic record
Abstract
"Conventional wisdom...and business school curricula...teaches us that making trade-offs is inevitable when it comes to hard choices. But sometimes, accepting the obvious trade-off just isn't good enough: the choices in front of us don't get us what we need. In those cases, rather than choosing the least worst option, we can use the models in front of us to create a new and better answer. This is integrative thinking. First introduced by Roger Martin in The Opposable Mind, integrative thinking is an approach to problem solving that uses opposing ideas as the basis for innovation. Now, in Creating Great Choices, Martin and fellow Rotman expert Jennifer Riel vividly show how they have refined and enhanced the understanding and practice of integrative thinking through their work teaching the concept and its principles to business and nonprofit executives, MBA students, even kids. Integrative thinking has been embraced by organizations such as Procter & Gamble, Deloitte, Verizon, and the Toronto District School Board...all seeking a replicable, thoughtful approach to creating a "third and better way" to make important choices in the face of unacceptable trade-offs. The book includes new stories of successful integrative thinkers that will demystify the process of creative problem solving. It lays out the authors' practical four-step methodology, which can be applied in virtually any context: Articulating opposing models Examining the models Generating possibilities Assessing prototypes Stimulating and practical, Creating Great Choices blends storytelling, theory, and hands-on advice to help any leader or manager facing a tough choice"...
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 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.020 | 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".