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?
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
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it