How institutional and ecological forces shape the career profiles of organizational leaders: An analysis of US law school deans, 1894–2009
Bibliographic record
Abstract
How do macro social forces shape the career profiles of organizational leaders? The aim of the article is to answer this question by examining how institutional and ecological forces have influenced the careers of law school deans in the US from the late 19th century to the present. Specifically, we focus on the coexistence of two social forces—professionalization and the diversity of an organizational population. On the one hand, we view professionalization as a converging institutional force that promotes homogeneity among leader career profiles. The diversity of an organizational population, on the other hand, is viewed as a diverging ecological force that increases heterogeneity among leader career profiles. We show how these two opposing forces have left different imprints on leader career profiles with a unique career data of 1396 deans in American law schools from 1894 to 2009. We utilize optimal matching analysis to assess the degree of similarity (or dissimilarity) among deans’ career sequences and test our hypotheses. This study contributes to our understanding of the link between macro social transformations and leader career profiles.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".