Bibliographic record
Abstract
In search of lessons to apply in our own careers, we often try to emulate what effective leaders do. Roger Martin says this focus is misplaced, because moves that work in one context may make little sense in another. A more productive, though more difficult, approach is to look at how such leaders think. After extensive interviews with more than 50 of them, the author discovered that most are integrative thinkers -that is, they can hold in their heads two opposing ideas at once and then come up with a new idea that contains elements of each but is superior to both. Martin argues that this process of consideration and synthesis (rather than superior strategy or faultless execution) is the hallmark of exceptional businesses and the people who run them. To support his point, he examines how integrative thinkers approach the four stages of decision making to craft superior solutions. First, when determining which features of a problem are salient, they go beyond those that are obviously relevant. Second, they consider multidirectional and nonlinear relationships, not just linear ones. Third, they see the whole problem and how the parts fit together. Fourth, they creatively resolve the tensions between opposing ideas and generate new alternatives. According to the author, integrative thinking is an ability everyone can hone. He points to several examples of business leaders who have done so, such as Bob Young, cofounder and former CEO of Red Hat, the dominant distributor of Linux opensource software. Young recognized from the beginning that he didn't have to choose between the two prevailing software business models. Inspired by both, he forged an innovative third way, creating a service offering for corporate customers that placed Red Hat on a path to tremendous success.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.024 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".