Exploring the Personal Histories of the Top Executives of<scp>US</scp>Firms Using a Quantitative Approach: Is There a Geographical Relationship with Corporate Headquarters, and Does It Influence Firm Performance?
Bibliographic record
Abstract
Abstract This paper analyses where top executives were born and where they attended university to uncover regional groupings of the most influential executives that shape corporate culture and strategy in the U nited S tates. Within the context of this paper, it is argued that the personal histories of top executives influence their decision‐making abilities, and thus corporate culture. It was found that intra‐regional, intra‐state, and intra‐city links were noteworthy factors in executive selection. Distances were higher, and percentages of intra‐regional links were lower for more profitable and higher growth firms. This indicates that more competitive firms acquire executives that have experienced different institutions during their lives and university educations. On the other hand, less profitable and lower growth firms are more likely to obtain executives embedded in similar institutions that already exist within the firm. The results suggest that key choices made by corporate America are influenced in part by geography far more complex than its own operations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".