Bibliographic record
Abstract
This study of 16 oil company CEOs, recognized as effective thinkers, found that they combined reason and intuition, primarily by relying on both explicit and ‘automatized ’ principles. The CEOs also shared three thinking-related traits: self-awareness, certain motivation, and an active mind. Suggestions for effective thinking are derived from the findings. “I have a strongly held belief that intuitive or gut feelings are just pattern recognition, almost instantaneous pattern recognition, whereas logic is the more painstaking process of making a pattern emerge. I think that one is just as important as the other.”--A CEO participating in the study To probe how ‘good minds ’ think, I asked 16 CEOs who ran (or had been running until recently) successful oil and gas companies to read a realistic decision scenario which presented three strategic alternatives: to invest in a new technology, to explore in the Arctic in a joint venture, or to acquire another oil company. The chief executives were then asked to think out loud how they would decide in the scenario. See Table I for the research methodology. This paper 1) explains how effective thinkers combined reason and intuition, 2) shows what principles they relied on, 3) describes the three common characteristics of effective thinkers, and 4) discusses implications of the study’s findings for those wanting to improve their thinking.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.502 | 0.159 |
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".